├── .idea ├── .gitignore ├── GDBMS_Paperlist.iml ├── inspectionProfiles │ └── profiles_settings.xml ├── misc.xml ├── modules.xml └── vcs.xml ├── AI4DB.md ├── CMU15721.md ├── In-Memory_DB.md ├── Paper ├── DistributeDatabase.md ├── FPGA_PaperList.md ├── GDBMS_PaperList.md ├── MPP_PaperList.md ├── NVM数据库技术.md ├── NewSQL数据库技术.md ├── RDMA相关数据库优化技术.md ├── 内存数据库技术.md ├── 多核CPU查询优化技术.md ├── 数据库一体机技术.md └── 新型存储器技术.md ├── Patent ├── Actian Vector专利.md ├── Altibase专利.md ├── ClickHouse 华为专利.md ├── EXASol 专利.md ├── Oracle 专利.md ├── SQL Server专利.md ├── alibaba analyticdb专利.md └── 数据库专利汇总（按技术分类）.md ├── README.md └── 简单操作指南.md /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Editor-based HTTP Client requests 5 | /httpRequests/ 6 | # Datasource local storage ignored files 7 | /dataSources/ 8 | /dataSources.local.xml 9 | -------------------------------------------------------------------------------- /.idea/GDBMS_Paperlist.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /AI4DB.md: -------------------------------------------------------------------------------- 1 | # AI4DB 2 | 3 | [AI4DB][ai4db] 4 | 5 | [ai4db]:https://github.com/LumingSun/ML4DB-paper-list -------------------------------------------------------------------------------- /CMU15721.md: -------------------------------------------------------------------------------- 1 | # CMU 15-721 course paper[link][cmu15721] 2 | 3 | [cmu15721]:https://15721.courses.cs.cmu.edu/spring2020/schedule.html 4 | 5 | ## Course Introduction and History of Databases (No In-Class Lecture) 6 | + M. Stonebraker, et al., What Goes Around Comes Around, in Readings in Database Systems, 4th Edition, 2006 (Optional) 7 | + A. Pavlo, et al., What's New with NewSQL?, in SIGMOD Record (vol. 45, iss. 2), 2016 (Optional) 8 | 9 | ## In-memory Database 10 | + X. Yu, et al., Staring into the Abyss: An Evaluation of Concurrency Control with One Thousand Cores, in VLDB, 2014 11 | + S. Harizopoulos, et al., OLTP Through the Looking Glass, and What We Found There, in SIGMOD, 2008 (Optional) 12 | + H. Garcia-Molina, et al., Main Memory Database Systems: An Overview, in IEEE Trans. on Knowl. and Data Eng., 1992 (Optional) 13 | 14 | [comment]: <> (## Multi-Version Concurrency Control (Design Decisions)) 15 | 16 | [comment]: <> (+ Y. Wu, et al., An Empirical Evaluation of In-Memory Multi-Version Concurrency Control, in VLDB, 2017 ) 17 | 18 | [comment]: <> (+ D.R.K. Ports, et al., Serializable Snapshot Isolation in PostgreSQL, in VLDB, 2012 (Optional)) 19 | 20 | [comment]: <> (+ Y. Huang, et al., Opportunities for Optimism in Contended Main-Memory Multicore Transactions, in VLDB, 2020 (Optional)) 21 | 22 | [comment]: <> (## Multi-Version Concurrency Control (Protocols)) 23 | 24 | [comment]: <> (+ T. Neumann, et al., Fast Serializable Multi-Version Concurrency Control for Main-Memory Database Systems, in SIGMOD, 2015) 25 | 26 | [comment]: <> (+ H. Lim, et al., Cicada: Dependably Fast Multi-Core In-Memory Transactions, in SIGMOD, 2017 (Optional)) 27 | 28 | [comment]: <> (+ P.-A. Larson, et al., High-Performance Concurrency Control Mechanisms for Main-Memory Databases, in VLDB, 2011 (Optional)) 29 | 30 | [comment]: <> (## Multi-Version Concurrency Control (Garbage Collection)) 31 | 32 | [comment]: <> (+ J. Böttcher, et al., Scalable Garbage Collection for In-Memory MVCC Systems, in VLDB, 2019) 33 | 34 | [comment]: <> (+ J. Lee, et al., Hybrid Garbage Collection for Multi-Version Concurrency Control in SAP HANA, in SIGMOD, 2016 (Optional)) 35 | 36 | ## OLTP Index(B+ Tree Data Structures) 37 | + Z. Wang, et al., Building A Bw-Tree Takes More Than Just Buzz Words, in SIGMOD, 2018 38 | + S.K. Cha, et al., Cache-Conscious Concurrency Control of Main-Memory Indexes on Shared-Memory Multiprocessor Systems, in VLDB, 2001 (Optional) 39 | + G. Graefe, A Survey of B-Tree Locking Techniques, in TODS, 2010 (Optional) 40 | + J. Levandoski, et al., The Bw-Tree: A B-tree for New Hardware, in ICDE, 2013 (Optional) 41 | + J. Faleiro, et al., Latch-free Synchronization in Database Systems: Silver Bullet or Fool's Gold?, in CIDR, 2017 (Optional) 42 | + T. Kissinger, et al., KISS-Tree: smart latch-free in-memory indexing on modern architectures, in DaMoN, 2012 (Optional) 43 | 44 | ## OLTP Index(Trie Data Structures) 45 | + V. Alvarez, et al., A Comparison of Adaptive Radix Trees and Hash Tables, in ICDE, 2015 46 | + V. Leis, et al., The Adaptive Radix Tree: ARTful Indexing for Main-Memory Databases, in ICDE, 2013 (Optional) 47 | + V. Leis, et al., The ART of Practical Synchronization, in DaMoN, 2016 (Optional) 48 | + R. Binna, et al., HOT: A Height Optimized Trie Index for Main-Memory Database Systems, in SIGMOD, 2018 (Optional) 49 | 50 | ## Storage Models, Data Layout, & System Catalogs 51 | + M. Athanassoulis, et al., Optimal Column Layout for Hybrid Workloads, in VLDB, 2019 52 | + J. Arulraj, et al., Bridging the Archipelago Between Row-Stores and Column-Stores for Hybrid Workloads, in SIGMOD, 2016 (Optional) 53 | + I. Alagiannis, et al., H2O: A Hands-free Adaptive Store, in SIGMOD, 2014 (Optional) 54 | + M. Grund, et al., HYRISE: a main memory hybrid storage engine, in VLDB, 2010 (Optional) 55 | + D. Abadi, et al., Column-Stores vs. Row-Stores: How Different Are They Really?, in SIGMOD, 2008 (Optional) 56 | 57 | ## Database Compression 58 | + D. Abadi, et al., Integrating Compression and Execution in Column-Oriented Database Systems, in SIGMOD, 2006 59 | + C. Binnig, et al., Dictionary-based Order-preserving String Compression for Main Memory Column Stores, in SIGMOD, 2009 (Optional) 60 | + I. Müller, et al., Adaptive String Dictionary Compression in In-Memory Column-Store Database Systems, in EDBT, 2014 (Optional) 61 | + V. Raman, et al., How to Wring a Table Dry: Entropy Compression of Relations and Querying of Compressed Relations, in VLDB, 2006 (Optional) 62 | + H. Zhang, et al., Reducing the Storage Overhead of Main-Memory OLTP Databases with Hybrid Indexes, in SIGMOD, 2016 (Optional) 63 | + C. Liu, et al., Mostly Order Preserving Dictionaries, in ICDE, 2019 (Optional) 64 | 65 | [comment]: <> (## Recovery Protocols) 66 | 67 | [comment]: <> (+ P. Antonopoulos, et al., Constant Time Recovery in Azure SQL Database, in VLDB, 2019 ) 68 | 69 | [comment]: <> (+ W. Zheng, et al., Fast Databases with Fast Durability and Recovery Through Multicore Parallelism, in OSDI, 2014 (Optional)) 70 | 71 | [comment]: <> (+ A. Goel, et al., Fast Database Restarts at Facebook, in SIGMOD, 2014 (Optional)) 72 | 73 | [comment]: <> (+ K. Ren, et al., Low-Overhead Asynchronous Checkpointing in Main-Memory Database Systems, in SIGMOD, 2016 (Optional)) 74 | 75 | [comment]: <> (+ N. Malviya, et al., Rethinking Main Memory OLTP Recovery, in ICDE, 2014 (Optional)) 76 | 77 | [comment]: <> (## Networking Protocols) 78 | 79 | [comment]: <> (+ M. Raasveldt, et al., Don't Hold My Data Hostage: A Case for Client Protocol Redesign, in VLDB, 2017 ) 80 | 81 | [comment]: <> (+ F. Li, et al., Accelerating Relational Databases by Leveraging Remote Memory and RDMA, in SIGMOD, 2016 (Optional)) 82 | 83 | [comment]: <> (+ F. Binnig, et al., The End of Slow Networks: It's Time for a Redesign, in VLDB, 2016 (Optional)) 84 | 85 | ## Scheduling 86 | + V. Leis, et al., Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age, in SIGMOD, 2014 87 | +I. Psaroudakis, et al., Scaling Up Concurrent Main-Memory Column-Store Scans: Towards Adaptive NUMA-aware Data and Task Placement, in VLDB, 2015 (Optional) 88 | + I. Psaroudakis, et al., Task Scheduling for Highly Concurrent Analytical and Transactional Main-Memory Workloads, in ADMS, 2013 (Optional) 89 | + D. Porobic, et al., OLTP on Hardware Islands, in VLDB, 2012 (Optional) 90 | 91 | ## Query Execution & Processing 92 | + M. Kester, et al., Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe?, in SIGMOD, 2017 93 | + P. Boncz, et al., MonetDB/X100: Hyper-Pipelining Query Execution, in CIDR, 2005 (Optional) 94 | + L. Shrinivas, et al., Materialization Strategies in the Vertica Analytic Database: Lessons Learned, in ICDE, 2013 (Optional) 95 | + A. Mishra, et al., Accelerating Analytics with Dynamic In-Memory Expressions, in VLDB, 2016 (Optional) 96 | 97 | ## Query Compilation 98 | + T. Neumann, Efficiently Compiling Efficient Query Plans for Modern Hardware, in VLDB, 2011 99 | + K. Krikellas, et al., Generating Code for Holistic Query Evaluation, in ICDE, 2010 (Optional) 100 | + H. Pirk, et al., CPU and Cache Efficient Management of Memory-Resident Databases, in ICDE, 2013 (Optional) 101 | + B. Raducanu, et al., Micro Adaptivity in Vectorwise, in SIGMOD, 2013 (Optional) 102 | + A. Shaikhha, et al., How to Architect a Query Compiler, in SIGMOD, 2016 (Optional) 103 | + A. Kohn, et al., Adaptive Execution of Compiled Queries, in ICDE, 2018 (Optional) 104 | 105 | ## Vectorized Execution 106 | + O. Polychroniou, et al., Rethinking SIMD Vectorization for In-Memory Databases, in SIGMOD, 2015 107 | + T. Thomas Willhalm, et al., SIMD-scan: Ultra Fast In-memory Table Scan using On-chip Vector Processing Units, in VLDB, 2009 (Optional) 108 | + Y. Li, et al., BitWeaving: Fast Scans for Main Memory Data Processing, in SIGMOD, 2013 (Optional) 109 | 110 | ## Vectorization vs. Compilation 111 | + T. Kersten, et al., Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask, in VLDB, 2018 112 | + P. Menon, et al., Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last, in VLDB, 2017 (Optional) 113 | + J. Sompolski, et al., Vectorization vs. Compilation in Query Execution, in DaMoN, 2011 (Optional) 114 | + H. Lang, et al., Data Blocks: Hybrid OLTP and OLAP on Compressed Storage using both Vectorization and Compilation, in SIGMOD, 2016 (Optional) 115 | 116 | ## Parallel Join Algorithms (Hashing) 117 | + S. Schuh, et al., An Experimental Comparison of Thirteen Relational Equi-Joins in Main Memory, in SIGMOD, 2016 118 | + S. Richter, et al., A Seven-Dimensional Analysis of Hashing Methods and its Implications on Query Processing, in VLDB, 2015 (Optional) 119 | + S. Blanas, et al., Design and Evaluation of Main Memory Hash Join Algorithms for Multi-core CPUs, in SIGMOD, 2011 (Optional) 120 | + C. Balkesen, et al., Main-Memory Hash Joins on Multi-Core CPUs: Tuning to the Underlying Hardware, in ICDE, 2013 (Optional) 121 | 122 | ## Parallel Join Algorithms (Sorting) 123 | + C. Balkesen, et al., Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited, in VLDB, 2013 124 | + C. Kim, et al., Sort vs. Hash Revisited: Fast Join Implementation on Modern Multi-Core CPUs, in VLDB, 2009 (Optional) 125 | + G. Graefe, et al., Sort vs. Hash Revisited, in TKDE, 1994 (Optional) 126 | + M.-C. Albutiu, et al., Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems, in VLDB, 2012 (Optional) 127 | 128 | ## Optimizer Implementation (Overview) 129 | + S. Chaudhuri, An Overview of Query Optimization in Relational Systems, in PODS, 1998 130 | + G. Graefe, et al., The Volcano Optimizer Generator: Extensibility and Efficient Search, in ICDE, 1993 (Optional) 131 | + G. Graefe, The Cascades Framework for Query Optimization, in IEEE Data Engineering Bulletin, 1995 (Optional) 132 | + M.A. Soliman, et al., Orca: A Modular Query Optimizer Architecture for Big Data, in SIGMOD, 2014 (Optional) 133 | + L.D. Shapiro, et al., Exploiting Upper and Lower Bounds In Top-Down Query Optimization, in IDEAS, 2001 (Optional) 134 | 135 | ## Optimizer Implementation (Top-Down vs. Bottom-Up) 136 | + Yongwen Xu, Efficiency in the Columbia Database Query Optimizer (pages 1-35), in Portland State University, 1998 137 | + J. Chen, et al., The MemSQL Query Optimizer, in VLDB, 2017 (Optional) 138 | + G. Moerkotte, et al., Dynamic Programming Strikes Back, in SIGMOD, 2008 (Optional) 139 | + T. Neumann, et al., The Complete Story of Joins (in HyPer), in BTW, 2017 (Optional) 140 | + E. Begoli, et al., Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources, in SIGMOD, 2018 (Optional) 141 | 142 | ## Optimizer Implementation (Alternative Approaches) 143 | + B. Ding, et al., Plan Stitch: Harnessing the Best of Many Plans, in VLDB, 2018 144 | + S. Babu, et al., Adaptive Query Processing in the Looking Glass, in CIDR, 2015 (Optional) 145 | + J. Zhu, et al., Looking Ahead Makes Query Plans Robust, in VLDB, 2017 (Optional) 146 | + R. Marcus, et al., Neo: A Learned Query Optimizer, in VLDB, 2019 (Optional) 147 | + I. Trummer, et al., SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning, in SIGMOD, 2019 (Optional) 148 | 149 | ## Cost Models 150 | + V. Leis, et al., How Good are Query Optimizers, Really?, in VLDB, 2015 151 | + M. Stillger, et al., LEO - DB2's LEarning Optimizer, in VLDB, 2001 (Optional) 152 | + Z. Yang, et al., Deep Unsupervised Cardinality Estimation, in VLDB, 2019 (Optional) 153 | + J. Sun, et al., An End-to-End Learning-based Cost Estimator, in VLDB, 2019 (Optional) 154 | + D. Vengerov, et al., Join Size Estimation Subject to Filter Conditions, in VLDB, 2015 (Optional) 155 | + Y. Chen, et al., Two-Level Sampling for Join Size Estimation, in SIGMOD, 2017 (Optional) 156 | 157 | ## Larger-than-Memory Databases 158 | + V. Leis, et al., LeanStore: In-Memory Data Management beyond Main Memory, in ICDE, 2018 159 | + J. DeBrabant, et al., Anti-Caching: A New Approach to Database Management System Architecture, in VLDB, 2013 (Optional) 160 | + R. Stoica, et al., Enabling Efficient OS Paging for Main-Memory OLTP Databases, in DaMoN, 2013 (Optional) 161 | + G. Graefe, et al., In-Memory Performance for Big Data, in VLDB, 2014 (Optional) 162 | + L. Ma, et al., Larger-than-memory Data Management on Modern Storage Hardware for In-memory OLTP Database Systems, in DaMoN, 2016 (Optional) 163 | 164 | ## Server-side Logic Execution 165 | + K. Ramachandra, et al., Froid: Optimization of Imperative Programs in a Relational Database, in VLDB, 2017 166 | + C. Duta, et al., Compiling PL/SQL Away, in CIDR, 2020 (Optional) 167 | 168 | ## Databases on New Hardware 169 | + J. Arulraj, et al., Write-Behind Logging, in VLDB, 2016 170 | + M. Owaida, et al., Centaur: A Framework for Hybrid CPU-FPGA Databases, in FCCM, 2017 (Optional) 171 | + H. Kimura, FOEDUS: OLTP Engine for a Thousand Cores and NVRAM, in SIGMOD, 2015 (Optional) 172 | + J. Power, et al., Toward GPUs Being Mainstream in Analytic Processing, in DaMoN, 2015 (Optional) 173 | -------------------------------------------------------------------------------- /In-Memory_DB.md: -------------------------------------------------------------------------------- 1 | # In-Memory Database 2 | 3 | ## SIGMOD 4 | 5 | + Yesdaulet Izenov, Asoke Datta, Florin Rusu, Jun Hyung Shin:COMPASS: Online Sketch-based Query Optimization for In-Memory Databases. SIGMOD Conference 2021: 804-816 2020 6 | 7 | + Chiranjeeb Buragohain, Knut Magne Risvik, Paul Brett, Miguel Castro, Wonhee Cho, Joshua Cowhig, Nikolas Gloy, Karthik Kalyanaraman, Richendra Khanna, John Pao, Matthew Renzelmann, Alex Shamis, Timothy Tan, Shuheng Zheng:A1: A Distributed In-Memory Graph Database. SIGMOD Conference 2020: 329-344 8 | 9 | + Matthias Jasny, Tobias Ziegler, Tim Kraska, Uwe Röhm, Carsten Binnig:DB4ML - An In-Memory Database Kernel with Machine Learning Support. SIGMOD Conference 2020: 159-173 10 | 11 | + Mingwei Samuel, Cong Yan, Alvin Cheung:Demonstration of Chestnut: An In-memory Data Layout Designer for Database Applications. SIGMOD Conference 2020: 2813-2816 2018 12 | 13 | + Mohamed S. Hassan, Tatiana Kuznetsova, Hyun Chai Jeong, Walid G. Aref, Mohammad Sadoghi:GRFusion: Graphs as First-Class Citizens in Main-Memory Relational Database Systems. SIGMOD Conference 2018: 1789-1792 14 | 15 | + Thomas Kissinger, Dirk Habich, Wolfgang Lehner:Adaptive Energy-Control for In-Memory Database Systems. SIGMOD Conference 2018: 351-364 16 | 17 | + Thomas Kissinger, Marcus Hähnel, Till Smejkal, Dirk Habich, Hermann Härtig, Wolfgang Lehner:Energy-Utility Function-Based Resource Control for In-Memory Database Systems LIVE. SIGMOD Conference 2018: 1717-1720 18 | 19 | + Alexander van Renen, Viktor Leis, Alfons Kemper, Thomas Neumann, Takushi Hashida, Kazuichi Oe, Yoshiyasu Doi, Lilian Harada, Mitsuru Sato:Managing Non-Volatile Memory in Database Systems. SIGMOD Conference 2018: 1541-1555 2017 20 | 21 | + Kayhan Dursun, Carsten Binnig, Ugur Çetintemel, Tim Kraska:Revisiting Reuse in Main Memory Database Systems. SIGMOD Conference 2017: 1275-1289 2016 22 | 23 | + Kun Ren, Thaddeus Diamond, Daniel J. Abadi, Alexander Thomson:Low-Overhead Asynchronous Checkpointing in Main-Memory Database Systems. SIGMOD Conference 2016: 1539-1551 24 | 25 | + Li Wang, Minqi Zhou, Zhenjie Zhang, Yin Yang, Aoying Zhou, Dina Bitton:Elastic Pipelining in an In-Memory Database Cluster. SIGMOD Conference 2016: 1279-1294 26 | 27 | + Chang Yao, Divyakant Agrawal, Gang Chen, Beng Chin Ooi, Sai Wu:Adaptive Logging: Optimizing Logging and Recovery Costs in Distributed In-memory Databases. SIGMOD Conference 2016: 1119-1134 28 | 29 | ## ICDE 30 | 31 | + Sukhada Pendse, Vasudha Krishnaswamy, Kartik Kulkarni, Yunrui Li, Tirthankar Lahiri, Vivekanandhan Raja, Jing Zheng, Mahesh Girkar, Akshay Kulkarni:Oracle Database In-Memory on Active Data Guard: Real-time Analytics on a Standby Database. ICDE 2020: 1570-1578 2019 32 | 33 | + Eugene Inseok Chong, Matthew Perry, Souripriya Das:Improving RDF Query Performance Using In-memory Virtual Columns in Oracle Database. ICDE 2019: 1814-1819 2018 34 | 35 | + Shasank Chavan, Albert Hopeman, Sangho Lee, Dennis Lui, Ajit Mylavarapu, Ekrem Soylemez:Accelerating Joins and Aggregations on the Oracle In-Memory Database. ICDE 2018: 1441-1452 36 | 37 | + Zhuhe Fang, Chuliang Weng, Li Wang, Aoying Zhou:Parallelizing Multiple Pipelines of One Query in a Main Memory Database Cluster. ICDE 2018: 1252-1255 38 | 39 | + Wolfgang Lehner, Dirk Habich, Till Kolditz:Teaching In-Memory Database Systems the Detection of Hardware Errors. ICDE 2018: 1663 40 | 41 | + Liang Li, Guoren Wang, Gang Wu, Ye Yuan:Consistent Snapshot Algorithms for In-Memory Database Systems: Experiments and Analysis. ICDE 2018: 1284-1287 2017 42 | 43 | + Zhongle Xie, Qingchao Cai, H. V. Jagadish, Beng Chin Ooi, Weng-Fai Wong:Parallelizing Skip Lists for In-Memory Multi-Core Database Systems. ICDE 2017: 119-122 2016 44 | 45 | + Niloy Mukherjee, Shasank Chavan, Maria Colgan, Mike Gleeson, Xiaoming He, Allison Holloway, Jesse Kamp, Kartik Kulkarni, Tirthankar Lahiri, Juan Loaiza, Neil MacNaughton, Atrayee Mullick, Sujatha Muthulingam, Vivekanandhan Raja, Raunak Rungta:Fault-tolerant real-time analytics with distributed Oracle Database In-memory. ICDE 2016: 1298-1309 46 | 47 | + David Schwalb, Martin Faust, Markus Dreseler, Pedro Flemming, Hasso Plattner:Leveraging non-volatile memory for instant restarts of in-memory database systems. ICDE 2016: 1386-1389 2015 48 | 49 | + Tirthankar Lahiri, Shasank Chavan, Maria Colgan, Dinesh Das, Amit Ganesh, Mike Gleeson, Sanket Hase, Allison Holloway, Jesse Kamp, Teck-Hua Lee, Juan Loaiza, Neil MacNaughton, Vineet Marwah, Niloy Mukherjee, Atrayee Mullick, Sujatha Muthulingam, Vivekanandhan Raja, Marty Roth, Ekrem Soylemez, Mohamed Zaït:Oracle Database In-Memory: A dual format in-memory database. ICDE 2015: 1253-1258 2014 50 | 51 | + Viktor Leis, Alfons Kemper, Thomas Neumann:Exploiting hardware transactional memory in main-memory databases. ICDE 2014: 580-591 52 | + Juchang Lee, Yong Sik Kwon, Franz Färber, Michael Muehle, Chulwon Lee, Christian Bensberg, Joo-Yeon Lee, Arthur H. Lee, Wolfgang Lehner:SAP HANA distributed in-memory database system: Transaction, session, and metadata management. ICDE 2013: 1165-1173 53 | 54 | + Justin J. Levandoski, Per-Åke Larson, Radu Stoica:Identifying hot and cold data in main-memory databases. ICDE 2013: 26-37 2011 55 | 56 | + Jan Schaffner, Benjamin Eckart, Dean Jacobs, Christian Schwarz, Hasso Plattner, Alexander Zeier:Predicting in-memory database performance for automating cluster management tasks. ICDE 2011: 1264-1275 2006 57 | 58 | + Antti-Pekka Liedes, Antoni Wolski:SIREN: A Memory-Conserving, Snapshot-Consistent Checkpoint Algorithm for in-Memory Databases. ICDE 2006: 99 1987 59 | 60 | + Dina Bitton, Maria Hanrahan, Carolyn Turbyfill:Performance of Complex Queries in Main Memory Database Systems. ICDE 1987: 72-81 61 | 62 | ## VLDB 63 | 64 | + phen Blott, Henry F. Korth:An Almost-Serial Protocol for Transaction Execution in Main-Memory Database Systems. VLDB 2002: 706-717 1997 65 | 66 | + Rajeev Rastogi, S. Seshadri, Philip Bohannon, Dennis W. Leinbaugh, Abraham Silberschatz, S. Sudarshan:Logical and Physical Versioning in Main Memory Databases. VLDB 1997: 86-95 67 | 68 | + Wee Teck Ng, Peter M. Chen:Integrating Reliable Memory in Databases. VLDB 1997: 76-85 1996 69 | 70 | + Sunita Sarawagi, Michael Stonebraker:Reordering Query Execution in Tertiary Memory Databases. VLDB 1996: 156-167 1995 71 | 72 | + Sunita Sarawagi:Query Processing in Tertiary Memory Databases. VLDB 1995: 585-596 1992 73 | 74 | + Michael J. Franklin, Michael J. Carey, Miron Livny:Global Memory Management in Client-Server Database Architectures. VLDB 1992: 596-609 75 | 76 | + Vibby Gottemukkala, Tobin J. Lehman:Locking and Latching in a Memory-Resident Database System. VLDB 1992: 533-544 1990 77 | 78 | + Charles R. Severance, Sakti Pramanik, P. Wolberg:Distributed Linear Hashing and Parallel Projection in Main Memory Databases. VLDB 1990: 674-682 1984 79 | 80 | + Andrea J. Borr:Robustness to Crash in a Distributed Database: A Non Shared-memory Multi-Processor Approach. VLDB 1984: 445-453 81 | 82 | ## VLDB J 83 | 84 | + Jian Fang, Yvo T. B. Mulder, Jan Hidders, Jinho Lee, H. Peter Hofstee:In-memory database acceleration on FPGAs: a survey. VLDB J. 29(1): 33-59 (2020)2018 85 | 86 | + Juchang Lee, Wook-Shin Han, Hyoung Jun Na, Chang Gyoo Park, Kyu Hwan Kim, Deok Hoe Kim, Joo-Yeon Lee, Sang Kyun Cha, SeungHyun Moon:Parallel replication across formats for scaling out mixed OLTP/OLAP workloads in main-memory databases. VLDB J. 27(3): 421-444 (2018)2017 87 | 88 | + Jan Finis, Robert Brunel, Alfons Kemper, Thomas Neumann, Norman May, Franz Färber:Order Indexes: supporting highly dynamic hierarchical data in relational main-memory database systems. VLDB J. 26(1): 55-80 (2017)1998 89 | 90 | + Wee Teck Ng, Peter M. Chen:Integrating Reliable Memory in Databases. VLDB J. 7(3): 194-204 (1998) 91 | 92 | ## VLDB Endow 93 | 94 | + Cheng Chen, Jun Yang, Mian Lu, Taize Wang, Zhao Zheng, Yuqiang Chen, Wenyuan Dai, Bingsheng He, Weng-Fai Wong, Guoan Wu, Yuping Zhao, Andy Rudoff:Optimizing An In-memory Database System For AI-powered On-line Decision Augmentation Using Persistent Memory. Proc. VLDB Endow. 14(5): 799-812 (2021)2020 95 | 96 | + Colin Adams, Luis Alonso, Benjamin Atkin, John Banning, Sumeer Bhola, Rick Buskens, Ming Chen, Xi Chen, Yoo Chung, Qin Jia, Nick Sakharov, George Talbot, Nick Taylor, Adam Tart:Monarch: Google's Planet-Scale In-Memory Time Series Database. Proc. VLDB Endow. 13(12): 3181-3194 (2020) 97 | 98 | + Takayuki Tanabe, Takashi Hoshino, Hideyuki Kawashima, Osamu Tatebe:An Analysis of Concurrency Control Protocols for In-Memory Database with CCBench. Proc. VLDB Endow. 13(13): 3531-3544 (2020) 99 | 100 | + Yu Xia, Xiangyao Yu, Andrew Pavlo, Srinivas Devadas:Taurus: Lightweight Parallel Logging for In-Memory Database Management Systems. Proc. VLDB Endow. 14(2): 189-201 (2020)2019 101 | 102 | + Tiago Rodrigo Kepe, Eduardo C. de Almeida, Marco A. Z. Alves:Database Processing-in-Memory: An Experimental Study. Proc. VLDB Endow. 13(3): 334-347 (2019) 103 | 104 | + Cong Yan, Alvin Cheung:Generating Application-specific Data Layouts for In-memory Databases. Proc. VLDB Endow. 12(11): 1513-1525 (2019)2018 105 | 106 | + Aditya Gurajada, Dheren Gala, Fei Zhou, Amit Pathak, Zhan-Feng Ma:BTrim - Hybrid In-Memory Database Architecture for Extreme Transaction Processing in VLDBs. Proc. VLDB Endow. 11(12): 1889-1901 (2018)2017 107 | 108 | + Prashanth Menon, Andrew Pavlo, Todd C. Mowry:Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last. Proc. VLDB Endow. 11(1): 1-13 (2017)2016 109 | 110 | + Yuan Yuan, Kaibo Wang, Rubao Lee, Xiaoning Ding, Jing Xing, Spyros Blanas, Xiaodong Zhang:BCC: Reducing False Aborts in Optimistic Concurrency Control with Low Cost for In-Memory Databases. Proc. VLDB Endow. 9(6): 504-515 (2016) 111 | 112 | + Steffen Zeuch, Holger Pirk, Johann-Christoph Freytag:Non-Invasive Progressive Optimization for In-Memory Databases. Proc. VLDB Endow. 9(14): 1659-1670 (2016)2015 113 | 114 | + Dinesh Das, Jiaqi Yan, Mohamed Zaït, Satyanarayana R. Valluri, Nirav Vyas, Ramarajan Krishnamachari, Prashant Gaharwar, Jesse Kamp, Niloy Mukherjee:Query Optimization in Oracle 12c Database In-Memory. Proc. VLDB Endow. 8(12): 1770-1781 (2015) 115 | 116 | + Niloy Mukherjee, Shasank Chavan, Maria Colgan, Dinesh Das, Mike Gleeson, Sanket Hase, Allison Holloway, Hui Jin, Jesse Kamp, Kartik Kulkarni, Tirthankar Lahiri, Juan Loaiza, Neil MacNaughton, Vineet Marwah, Atrayee Mullick, Andy Witkowski, Jiaqi Yan, Mohamed Zaït:Distributed Architecture of Oracle Database In-memory. Proc. VLDB Endow. 8(12): 1630-1641 (2015) 117 | 118 | + Vivek R. Narasayya, Ishai Menache, Mohit Singh, Feng Li, Manoj Syamala, Surajit Chaudhuri:Sharing Buffer Pool Memory in Multi-Tenant Relational Database-as-a-Service. Proc. VLDB Endow. 8(7): 726-737 (2015) 119 | 120 | + Tuomas Pelkonen, Scott Franklin, Paul Cavallaro, Qi Huang, Justin Meza, Justin Teller, Kaushik Veeraraghavan:Gorilla: A Fast, Scalable, In-Memory Time Series Database. Proc. VLDB Endow. 8(12): 1816-1827 (2015)2014 121 | 122 | + Ahmed Eldawy, Justin J. Levandoski, Per-Åke Larson:Trekking Through Siberia: Managing Cold Data in a Memory-Optimized Database. Proc. VLDB Endow. 7(11): 931-942 (2014) 123 | 124 | + Hasso Plattner:The Impact of Columnar In-Memory Databases on Enterprise Systems. Proc. VLDB Endow. 7(13): 1722-1729 (2014)2012 125 | 126 | + Martina-Cezara Albutiu, Alfons Kemper, Thomas Neumann:Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems. Proc. VLDB Endow. 5(10): 1064-1075 (2012) 127 | 128 | + Felix Halim, Stratos Idreos, Panagiotis Karras, Roland H. C. Yap:Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores. Proc. VLDB Endow. 5(6): 502-513 (2012)2009 129 | 130 | + Yanif Ahmad, Christoph Koch:DBToaster: A SQL Compiler for High-Performance Delta Processing in Main-Memory Databases. Proc. VLDB Endow. 2(2): 1566-1569 (2009) 131 | -------------------------------------------------------------------------------- /Paper/DistributeDatabase.md: -------------------------------------------------------------------------------- 1 | ## Distribute Database 2 | 3 | ### Survey 4 | + Alan Hong, Caiping Sun, Meixuan Chen:A Survey of Distributed Database Systems based on Blockchain. SmartBlock 2020: 191-196 5 | 6 | 7 | ### New Technology 8 | + Sara Bergman, Mikael Asplund, Simin Nadjm-Tehrani:Permissioned blockchains and distributed databases: A performance study. Concurr. Comput. Pract. Exp. 32(12) (2020) 9 | + Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao:Declarative Recursive Computation on an RDBMS: or, Why You Should Use a Database For Distributed Machine Learning. SIGMOD Rec. 49(1): 43-50 (2020) 10 | + Anuradha Karunarathna, Dinika Senarath, Shalika Madhushanki, Chinthaka Weerakkody, Miyuru Dayarathna, Sanath Jayasena, Toyotaro Suzumura:Scalable Graph Convolutional Network based Link Prediction on a Distributed Graph Database Server. CLOUD 2020: 107-115 11 | + Junwei Liang, Maode Ma:Incremental Database Based on Distributed Ledger Technology for IDSs. GLOBECOM 2020: 1-6 12 | + Arash Fard, Anh Le, George Larionov, Waqas Dhillon, Chuck Bear:Vertica-ML: Distributed Machine Learning in Vertica Database. SIGMOD Conference 2020: 755-768 13 | + Chiranjeeb Buragohain, Knut Magne Risvik, Paul Brett, Miguel Castro, Wonhee Cho, Joshua Cowhig, Nikolas Gloy, Karthik Kalyanaraman, Richendra Khanna, John Pao, Matthew Renzelmann, Alex Shamis, Timothy Tan, Shuheng Zheng:A1: A Distributed In-Memory Graph Database. CoRR abs/2004.05712 (2020) 14 | 15 | ### System 16 | + Pingcheng Ruan, Tien Tuan Anh Dinh, Dumitrel Loghin, Meihui Zhang, Gang Chen, Qian Lin, Beng Chin Ooi:Blockchains vs. Distributed Databases: Dichotomy and Fusion. SIGMOD Conference 2021: 1504-1517 17 | + Lamine Diop, Cheikh Talibouya Diop, Arnaud Giacometti, Arnaud Soulet:Pattern on demand in transactional distributed databases. Inf. Syst. 104: 101908 (2022) 18 | + David Luaces, José R. R. Viqueira, José Manuel Cotos, Julián C. Flores:Efficient access methods for very large distributed graph databases. Inf. Sci. 573: 65-81 (2021) 19 | + Yong Wang, Guobin Hou:Development of Island tourism resources based on distributed database and embedded system. Microprocess. Microsystems 81: 103752 (2021) 20 | + Patrick Valduriez, Ricardo Jiménez-Peris, M. Tamer Özsu:Distributed Database Systems: The Case for NewSQL. Trans. Large Scale Data Knowl. Centered Syst. 48: 1-15 (2021) 21 | + Prakash Jha, Navneet Gangwar, Himanshu Mani Tripathi, Tareq Bin Ahammed, Samim Jahin, Ripon Patgiri:AxomDB: A Distributed and Scalable In-memory Database System. OCIT 2021: 20-25 22 | + Rodrigo Laigner, Yongluan Zhou, Marcos Antonio Vaz Salles:A distributed database system for event-based microservices. DEBS 2021: 25-30 23 | + Yong-Feng Ge, Jinli Cao, Hua Wang, Zhenxiang Chen, Yanchun Zhang:Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation. Data Sci. Eng. 6(4): 380-391 (2021) 24 | + Hua Guo, Xuan Zhou, Le Cai:Lock Violation for Fault-tolerant Distributed Database System*. ICDE 2021: 1416-1427 25 | + Shereen Ismail, Eman AlKhader, Aydan Gasimova, Hassan Reza:A lightweight Distributed Database Management in WSN. ISNCC 2021: 1-5 26 | + Kia Rahmani, Kartik Nagar, Benjamin Delaware, Suresh Jagannathan:Repairing serializability bugs in distributed database programs via automated schema refactoring. PLDI 2021: 32-47 27 | + Wided Ben Abid, Mohamed Ben Ahmed Mhiri, Emna Bouazizi, Faïez Gargouri:Ontological Data Replication in a Distributed Real-Time Database System. SoMeT 2021: 567-580 28 | + Yaser Mansouri, Victor Prokhorenko, Faheem Ullah, Muhammad Ali Babar:Evaluation of Distributed Databases in Hybrid Clouds and Edge Computing: Energy, Bandwidth, and Storage Consumption. CoRR abs/2109.07260 (2021) 29 | + Michael Abebe, Brad Glasbergen, Khuzaima Daudjee:MorphoSys: Automatic Physical Design Metamorphosis for Distributed Database Systems. Proc. VLDB Endow. 13(13): 3573-3587 (2020) 30 | + Lamine Diop, Cheikh Talibouya Diop, Arnaud Giacometti, Arnaud Soulet:Pattern Sampling in Distributed Databases. ADBIS 2020: 60-74 31 | + Ana Almeida, Francisco Oliveira, Rui Lebre, Carlos Costa:NoSQL distributed database for DICOM objects. BIBM 2020: 1882-1885 32 | + Ou-Ya Pei, Zhanhuai Li, Wenjie Liu, Hongtao Du:A Novel Scalable Distributed Database System. CIPAE 2020: 31-36 33 | + Chiranjeeb Buragohain, Knut Magne Risvik, Paul Brett, Miguel Castro, Wonhee Cho, Joshua Cowhig, Nikolas Gloy, Karthik Kalyanaraman, Richendra Khanna, John Pao, Matthew Renzelmann, Alex Shamis, Timothy Tan, Shuheng Zheng:A1: A Distributed In-Memory Graph Database. SIGMOD Conference 2020: 329-344 34 | + Mathieu B. Demarne, Jim Gramling, Tomer Verona, Miso Cilimdzic:Reliability Analytics for Cloud Based Distributed Databases. SIGMOD Conference 2020: 1479-1492 35 | + Rebecca Taft, Irfan Sharif, Andrei Matei, Nathan VanBenschoten, Jordan Lewis, Tobias Grieger, Kai Niemi, Andy Woods, Anne Birzin, Raphael Poss, Paul Bardea, Amruta Ranade, Ben Darnell, Bram Gruneir, Justin Jaffray, Lucy Zhang, Peter Mattis:CockroachDB: The Resilient Geo-Distributed SQL Database. SIGMOD Conference 2020: 1493-1509 36 | + Rachid Zennou, Ranadeep Biswas, Ahmed Bouajjani, Constantin Enea, Mohammed Erradi:Checking Causal Consistency of Distributed Databases. CoRR abs/2011.09753 (2020) 37 | 38 | 39 | ### Architecture 40 | + Somenath Chakraborty, Dia Ali, Beddhu Murali:A Novel Distributed Database Architectural Model for Mobile Cloud Computing. CoRR abs/2202.09960 (2022) 41 | + R. Uday Kiran, Sadanori Ito, Minh-Son Dao, Koji Zettsu, Cheng-Wei Wu, Yutaka Watanobe, Incheon Paik, Truong Cong Thang:Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture. IEEE BigData 2020: 4724-4733 42 | 43 | ### Model 44 | + Steffen Kläbe, Kai-Uwe Sattler, Stephan Baumann:PatchIndex: exploiting approximate constraints in distributed databases. Distributed Parallel Databases 39(3): 833-853 (2021) 45 | + Zhanhao Zhao:Efficiently Supporting Adaptive Multi-Level Serializability Models in Distributed Database Systems. SIGMOD Conference 2021: 2908-2910 46 | + Jack Waudby, Paul D. Ezhilchelvan, Jim Webber, Isi Mitrani:Preserving reciprocal consistency in distributed graph databases. PaPoC@EuroSys 2020: 2:1-2:7 47 | 48 | ### Method 49 | + Hui Cao:Big data attribute selection method in distributed network fault diagnosis database. J. Intell. Fuzzy Syst. 38(6): 7903-7914 (2020) 50 | + K. V. Sushena Sree, Prasad Krishnan:Coded Data Rebalancing for Decentralized Distributed Databases. CoRR abs/2010.11935 (2020) 51 | 52 | ### Algorithm 53 | + Yong-Feng Ge, Wei-jie Yu, Jinli Cao, Hua Wang, Zhi-Hui Zhan, Yanchun Zhang, Jun Zhang:Distributed Memetic Algorithm for Outsourced Database Fragmentation. IEEE Trans. Cybern. 51(10): 4808-4821 (2021) 54 | + Sayed A. Mohsin, Ahmed Younes, Saad M. Darwish:Dynamic Cost Ant Colony Algorithm to Optimize Query for Distributed Database Based on Quantum-Inspired Approach. Symmetry 13(1): 70 (2021) 55 | + Nuparam Chauhan, Surya Prakash Tripathi:Optimal Admission Control Policy Based on Memetic Algorithm in Distributed Real Time Database System. Wirel. Pers. Commun. 117(2): 1123-1141 (2021) 56 | + Ahmed M. Khedr, P. V. Pravija Raj:DRNNA: Decomposable Reverse Nearest Neighbor Algorithm for Vertically Distributed Databases. SSD 2021: 681-686 57 | + Sayed A. Mohsin, Saad M. Darwish, Ahmed Younes:Dynamic Cost Ant Colony Algorithm for Optimize Distributed Database Query. AICV 2020: 170-181 58 | + Mohammed Hussein Abdalla, Murat Karabatak:To Review and Compare Evolutionary Algorithms in Optimization of Distributed Database Query. ISDFS 2020: 1-5 59 | + Houssem Mansouri, Al-Sakib Khan Pathan:A Communication-Induced Checkpointing Algorithm for Consistent-Transaction in Distributed Database Systems. SSCC 2020: 21-32 60 | + Niranjan Rai, Xiang Lian:Probabilistic Top-k Dominating Queries in Distributed Uncertain Databases (Technical Report). CoRR abs/2105.04486 (2021) 61 | 62 | ### Performance 63 | + Sayed A. Mohsin, Saad Mohamed Darwish, Ahmed Younes:QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database. IEEE Access 9: 15833-15846 (2021) 64 | + Lucie Svitáková, Michal Valenta, Jaroslav Pokorný:Enhanced adaptive partitioning in a distributed graph database. J. Inf. Telecommun. 5(1): 104-120 (2021) 65 | + Johannes Wirth, Jaco A. Hofmann, Lasse Thostrup, Carsten Binnig, Andreas Koch:Scalable and Flexible High-Performance In-Network Processing of Hash Joins in Distributed Databases. FPT 2021: 1-9 66 | + Yaofeng Tu, Yinjun Han, Hao Jin, Zhenghua Chen, Yanchao Zhao:RDMA Based Performance Optimization on Distributed Database Systems: A Case Study with GoldenX. WASA (2) 2021: 237-248 67 | + Yaser Mansouri, Victor Prokhorenko, Muhammad Ali Babar:An automated implementation of hybrid cloud for performance evaluation of distributed databases. J. Netw. Comput. Appl. 167: 102740 (2020) 68 | + Manuel Weißbach, Hannes Hilbert, Thomas Springer:Performance Analysis of Continuous Binary Data Processing using Distributed Databases within Stream Processing Environments. CLOSER 2020: 138-149 69 | + Jero Mario Schäfer, Ulrich Sax, Lena Wiese:Benchmarking a distributed database design that supports patient cohort identification. IDEAS 2020: 18:1-18:8 70 | + Yaser Mansouri, Muhammad Ali Babar:The Impact of Distance on Performance and Scalability of Distributed Database Systems in Hybrid Clouds. CoRR abs/2007.15826 (2020) 71 | 72 | ### Optimization 73 | + Manizheh Yazdani, Nima Jafari Navimipour:Join query optimisation in the distributed databases using a hybrid harmony search and artificial bee colony algorithm. Int. J. Bio Inspired Comput. 17(3): 189-198 (2021) 74 | + Bingxu Zheng, Xiang Li, Zhenzhen Tian, Limin Meng:Optimization Method for Distributed Database Query Based on an Adaptive Double Entropy Genetic Algorithm. IEEE Access 10: 4640-4648 (2022) 75 | + Shikha Mehta, Parul Agarwal, Prakhar Shrivastava, Jharna Barlawala:Differential bond energy algorithm for optimal vertical fragmentation of distributed databases. J. King Saud Univ. Comput. Inf. Sci. 34(1): 1466-1471 (2022) 76 | + George Kousiouris, Dimosthenis Kyriazis:Enabling Containerized, Parametric and Distributed Database Deployment and Benchmarking as a Service. ICPE (Companion) 2021: 77-80 77 | + Mykhailo Dvoretskyi, Svitlana Dvoretska, Hlib Horban, Yuriy Nezdoliy:Optimization of the Database Structure of a Distributed Corporate Information System Node Using the Analytic Hierarchy Process. IT&I Workshops 2020: 193-203 78 | + Ashraf Mahgoub, Alexander Medoff, Rakesh Kumar, Subrata Mitra, Ana Klimovic, Somali Chaterji, Saurabh Bagchi:OPTIMUSCLOUD: Heterogeneous Configuration Optimization for Distributed Databases in the Cloud. USENIX Annual Technical Conference 2020: 189-203 79 | 80 | ### Application 81 | + Bilge Kaan Karamete, Louaï Adhami, Eli Glaser:A fixed storage distributed graph database hybrid with at-scale OLAP expression and I/O support of a relational DB: Kinetica-Graph. CoRR abs/2201.02136 (2022) 82 | + Ahmed M. Khedr, Zaher Al Aghbari, Amal Al Ali, Mariam Eljamil:An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases. IEEE Access 9: 15320-15333 (2021) 83 | + Micael Pedrosa, Rui Lebre, Carlos Costa:A Performant Protocol for Distributed Health Records Databases. IEEE Access 9: 125930-125940 (2021) 84 | + P. Sathishkumar, M. Gunasekaran:An improved vertical fragmentation, allocation and replication for enhancing e-learning in distributed database environment. Comput. Intell. 37(1): 253-272 (2021) 85 | + Yi Lu, Xiangyao Yu, Lei Cao, Samuel Madden:Epoch-based Commit and Replication in Distributed OLTP Databases. Proc. VLDB Endow. 14(5): 743-756 (2021) 86 | + Min Dong, Haozhao Zhong, Boyu Sun, Sheng Bi, Yi Cai:SardineDB: A Distributed Database on the Edge of the Network. APWeb/WAIM (2) 2021: 186-193 87 | + Johannes Wirth, Jaco A. Hofmann, Lasse Thostrup, Andreas Koch, Carsten Binnig:Exploiting 3D Memory for Accelerated In-Network Processing of Hash Joins in Distributed Databases. ARC 2021: 18-32 88 | + Rui Lebre, Carlos Costa:A non-relational approach for distributed medical imaging databases. BHI 2021: 1-6 89 | + Song Gao, Bohua Zhan, Depeng Liu, Xuechao Sun, Yanan Zhi, David N. Jansen, Lijun Zhang:Formal Verification of Consensus in the Taurus Distributed Database. FM 2021: 741-751 90 | + Kia Rahmani, Kartik Nagar, Benjamin Delaware, Suresh Jagannathan:Repairing Serializability Bugs in Distributed Database Programs via Automated Schema Refactoring. CoRR abs/2103.05573 (2021) 91 | + Guangzhou Yu, Weina Fu:Analysis of distributed database access path prediction based on recurrent neural network in internet of things. Concurr. Comput. Pract. Exp. 32(1) (2020) 92 | + Jin-Tao Gao, Wenjie Liu, Zhanhuai Li:An adaptive strategy for statistics collecting in distributed database. Frontiers Comput. Sci. 14(5): 145610 (2020) 93 | + Xiaowen Feng, Pengcheng Deng, Yanzi Yi, Qi Yu, Decun Luo, Hua Deng, Yujue Wang:Verifiable Decentralized Access Control for Distributed Databases. CyberC 2020: 248-253 94 | + Keita Katagiri, Takeo Fujii:Measurement-based Spectrum Database for Wireless Distributed Networks Using Shadowing Classifier. ICAIIC 2020: 64-69 95 | + Yi Wu, Guang-Zhong Cao, Su-Dan Huang, Yeping Peng:Distributed Database of Cloud Platform for the Lower-limb Exoskeleton Robot. UR 2020: 322-326 96 | + Joaquim Dias Garcia, Guilherme Machado, André Dias, Gerson Couto Oliveira, John Ollis, John Fazio, Daniel Hua:Multiscale Stochastic Simulation of the US Pacific Northwest Using Distributed Computing and Databases with Integrated Inflow and Variable Renewable Energy. CoRR abs/2008.02155 (2020) 97 | 98 | ### Query Processing 99 | + Ralf Hartmut Güting, Thomas Behr, Jan Kristof Nidzwetzki:Distributed arrays: an algebra for generic distributed query processing. Distributed Parallel Databases 39(4): 1009-1064 (2021) 100 | + Leanne Rylands, Jennifer Seberry, Xun Yi, Andrei Kelarev, Joe Ryan, Yuqing Lin:Collusion-resistant protocols for private processing of aggregated queries in distributed databases. Distributed Parallel Databases 39(1): 97-127 (2021) 101 | + Muon Ha, Yulia A. Shichkina:Translation of Query for the Distributed Document Database. ICCSA (8) 2021: 396-405 102 | + Jin-Tao Gao, Wenjie Liu, Zhanhuai Li, Jian Zhang, Li Shen:A general fragments allocation method for join query in distributed database. Inf. Sci. 512: 1249-1263 (2020) 103 | + Xu Yuan, Xingliang Yuan, Yihe Zhang, Baochun Li, Cong Wang:Enabling Encrypted Boolean Queries in Geographically Distributed Databases. IEEE Trans. Parallel Distributed Syst. 31(3): 634-646 (2020) 104 | 105 | ### Storage Manager 106 | + G. M. Siddesh, S. R. Mani Sekhar, Vighnesh S., Nikhila Sai, Deepthi Sai, Sanjana D.:Distributed Database Management With Integration of Blockchain and Long Short-Term Memory. Int. J. Inf. Retr. Res. 11(3): 18-33 (2021) 107 | + Zijian Li, Chuqiao Xiao:ER-Store: A Hybrid Storage Mechanism with Erasure Coding and Replication in Distributed Database Systems. Sci. Program. 2021: 9910942:1-9910942:13 (2021) 108 | + Arjan Singh, Baljit Singh Khehra, Bhupinder Singh Mavi:Simplified-BBO for Non-redundant Allocation of Data in Distributed Database Design. MWSCAS 2021: 544-548 109 | + Masood Niazi Torshiz, Azadeh Salehi Esfaji, Haleh Amintoosi:Enhanced schemes for data fragmentation, allocation, and replication in distributed database systems. Comput. Syst. Sci. Eng. 35(2) (2020) 110 | + Lucie Svitáková, Michal Valenta, Jaroslav Pokorný:Effective Data Redistribution Based on User Queries in a Distributed Graph Database. ACIIDS (2) 2020: 218-229 111 | + Laurent Bindschaedler, Ashvin Goel, Willy Zwaenepoel:Hailstorm: Disaggregated Compute and Storage for Distributed LSM-based Databases. ASPLOS 2020: 301-316 112 | 113 | ### Security 114 | + Mahboob Qaosar, Asif Zaman, Md. Anisuzzaman Siddique, Chen Li, Yasuhiko Morimoto:Secure k-skyband computation framework in distributed multi-party databases. Inf. Sci. 515: 388-403 (2020) 115 | + Phillipp Schoppmann, Lennart Vogelsang, Adrià Gascón, Borja Balle:Secure and Scalable Document Similarity on Distributed Databases: Differential Privacy to the Rescue. Proc. Priv. Enhancing Technol. 2020(2): 209-229 (2020) 116 | + Mehmet Guclu, Cigdem Bakir, Veli Hakkoymaz:A New Scalable and Expandable Access Control Model for Distributed Database Systems in Data Security. Sci. Program. 2020: 8875069:1-8875069:10 (2020) 117 | + Mengyuan Zhang, Jiming Chen, Shibo He, Lei Yang, Xiaowen Gong, Junshan Zhang:Privacy-Preserving Database Assisted Spectrum Access for Industrial Internet of Things: A Distributed Learning Approach. IEEE Trans. Ind. Electron. 67(8): 7094-7103 (2020) 118 | 119 | ### Book 120 | + M. Tamer Özsu, Patrick Valduriez:Principles of Distributed Database Systems, 4th Edition. Springer 2020, ISBN 978-3-030-26252-5, pp. 1-674 121 | 122 | ### Evaluation 123 | + Yaser Mansouri, Victor Prokhorenko, Muhammad Ali Babar:An Automated Implementation of Hybrid Cloud for Performance Evaluation of Distributed Databases. CoRR abs/2006.02833 (2020) 124 | + Ahmet Ercan Topcu, Aimen Mukhtar Rmis:Analysis and evaluation of the riak cluster environment in distributed databases. Comput. Stand. Interfaces 72: 103452 (2020) 125 | 126 | ### Other 127 | + Martin Kleppmann:Thinking in events: from databases to distributed collaboration software: keynote at the 15th ACM International Conference on Distributed and Event-Based Systems (DEBS). DEBS 2021: 15-24 128 | + Anatoliy Gorbenko, Andrii Karpenko, Olga Tarasyuk:Analysis of Trade-offs in Fault-Tolerant Distributed Computing and Replicated Databases. DESSERT 2020: 1-6 129 | + Le Danh Tai, Kim Thang Nguyen, Ta Minh Thanh:Distributed Watermarking for Cross-Domain of Semantic Large Image Database. INISCOM 2020: 163-180 130 | + K. V. Sushena Sree, Prasad Krishnan:Coded Data Rebalancing for Decentralized Distributed Databases. ITW 2020: 1-5 131 | -------------------------------------------------------------------------------- /Paper/FPGA_PaperList.md: -------------------------------------------------------------------------------- 1 | # FPGA_Paper_List 2 | 3 | ## Query execution 4 | 5 | - Fabio Maschi, Muhsen Owaida, Gustavo Alonso, Matteo Casalino, Anthony Hock-Koon:Making Search Engines Faster by Lowering the Cost of Querying Business Rules Through FPGAs. SIGMOD Conference 2020: 2255-2270 6 | - Mohammedreza Najafi, Mohammad Sadoghi, Hans-Arno Jacobsen:Flexible Query Processor on FPGAs. Proc. VLDB Endow. 6(12): 1310-1313 (2013) 7 | - Mehdi Moghaddamfar, Christian Färber, Wolfgang Lehner, Norman May, Akash Kumar:Resource-Efficient Database Query Processing on FPGAs. DaMoN 2021: 4:1-4:8 8 | - Mohammad Sadoghi, Rija Javed, Naif Tarafdar, Harsh Singh, Rohan Palaniappan, Hans-Arno Jacobsen:Multi-query Stream Processing on FPGAs. ICDE 2012: 1229-1232 9 | - Johns Paul, Bingsheng He, Chiew Tong Lau:Query Processing on OpenCL-Based FPGAs: Challenges and Opportunities. ICPADS 2018: 937-945 10 | - Ze-ke Wang, Hui Yan Cheah, Johns Paul, Bingsheng He, Wei Zhang:Accelerating Database Query Processing on OpenCL-based FPGAs (Abstract Only). FPGA 2016: 274 11 | - Lekshmi B. G., Andreas Becher, Klaus Meyer-Wegener, Stefan Wildermann, Jürgen Teich: 12 | SQL Query Processing Using an Integrated FPGA-based Near-Data Accelerator in ReProVide. EDBT 2020: 639-642 13 | - Ze-ke Wang, Johns Paul, Hui Yan Cheah, Bingsheng He, Wei Zhang:Relational query processing on OpenCL-based FPGAs. FPL 2016: 1-10 14 | - Louis Woods, Zsolt István, Gustavo Alonso:Hybrid FPGA-accelerated SQL query processing. FPL 2013: 1 15 | - Christopher Dennl, Daniel Ziener, Jürgen Teich:On-the-fly Composition of FPGA-Based SQL Query Accelerators Using a Partially Reconfigurable Module Library. FCCM 2012: 45-52 16 | - David Sidler, Zsolt István, Muhsen Owaida, Gustavo Alonso:Accelerating Pattern Matching Queries in Hybrid CPU-FPGA Architectures. SIGMOD Conference 2017: 403-415 17 | - René Müller, Jens Teubner, Gustavo Alonso:Data Processing on FPGAs. Proc. VLDB Endow. 2(1): 910-921 (2009) 18 | - Jens Teubner, Louis Woods:Data Processing on FPGAs. Synthesis Lectures on Data Management, Morgan & Claypool Publishers 2013, ISBN 9781627050609, pp. 1-118 19 | - Xinyu Chen, Hongshi Tan, Yao Chen, Bingsheng He, Weng-Fai Wong, Deming Chen:ThunderGP: HLS-based Graph Processing Framework on FPGAs. FPGA 2021: 69-80 20 | 21 | ## Query optimization 22 | 23 | - Lekshmi Beena Gopalakrishnan Nair:Capability Aware Query Optimizer For An FPGA-Based Near-Data Processor. University of Erlangen-Nuremberg, Germany, 2021 24 | 25 | - Kento Miura, Toshiyuki Amagasa, Hiroyuki Kitagawa:Accelerating Regular Path Queries using FPGA. ADMS@VLDB 2019: 47-54 26 | 27 | - Linhuai Tang, Gang Cai, Yong Zheng, Jiamin Chen:A Resource and Performance Optimization Reduction Circuit on FPGAs. IEEE Trans. Parallel Distributed Syst. 32(2): 355-366 (2021) 28 | 29 | - Zeyad A. Al-Odat, Mazhar Ali, Assad Abbas, Samee U. Khan:Secure Hash Algorithms and the Corresponding FPGA Optimization Techniques. ACM Comput. Surv. 53(5): 97:1-97:36 (2020) 30 | 31 | ## Operator operation 32 | 33 | - Meiting Xue, Qianjian Xing, Chen Feng, Feng Yu, Zhen-Guo Ma:FPGA-Accelerated Hash Join Operation for Relational Databases. IEEE Trans. Circuits Syst. II Express Briefs 67-II(10): 1919-1923 (2020) 34 | - Xinyu Chen, Yao Chen, Ronak Bajaj, Jiong He, Bingsheng He, Weng-Fai Wong, Deming Chen:Is FPGA Useful for Hash Joins? CIDR 2020 35 | - Wen-Qi Wu, Meiting Xue, Qianjian Xing, Feng Yu:High-Parallelism Hash-Merge Architecture for Accelerating Join Operation on FPGA. IEEE Trans. Circuits Syst. II Express Briefs 68(7): 2650-2654 (202) 36 | - Wen-Qi Wu, Meiting Xue, Tian-Qi Zhu, Zhen-Guo Ma, Feng Yu:High-Throughput Parallel SRAM-Based Hash Join Architecture on FPGA. IEEE Trans. Circuits Syst. 67-II(11): 2502-2506 (2020) 37 | - Meiting Xue, Qianjian Xing, Chen Feng, Feng Yu, Zhen-Guo Ma:FPGA-Accelerated Hash Join Operation for Relational Databases. IEEE Trans. Circuits Syst. II Express Briefs 67-II(10): 1919-1923 (2020) 38 | - Huan Zhang, Bei Zhao, Wei-Jun Li, Zhen-Guo Ma, Feng Yu:Resource-Efficient Parallel Tree-Based Join Architecture on FPGA. IEEE Trans. Circuits Syst. II Express Briefs 66-II(1): 111-115 (2019) 39 | - Robert J. Halstead, Ildar Absalyamov, Walid A. Najjar, Vassilis J. Tsotras:FPGA-based Multithreading for In-Memory Hash Joins. CIDR 2015 40 | - Robert Lasch, Mehdi Moghaddamfar, Norman May, Süleyman Sirri Demirsoy, Christian Färber, Kai-Uwe Sattler:Bandwidth-optimal Relational Joins on FPGAs. EDBT 2022: 1:27-1:39 41 | - Robert J. Halstead, Bharat Sukhwani, Hong Min, Mathew Thoennes, Parijat Dube, Sameh W. Asaad, Balakrishna Iyer:Accelerating Join Operation for Relational Databases with FPGAs. FCCM 2013: 17-20 42 | - Behzad Salami, Oriol Arcas-Abella, Nehir Sönmez:HATCH: Hash Table Caching in Hardware for Efficient Relational Join on FPGA. FCCM 2015: 163 43 | - Charalabos Kritikakis, Grigorios Chrysos, Apostolos Dollas, Dionisios N. Pnevmatikatos:An FPGA-based high-throughput stream join architecture. FPL 2016: 1-4 44 | - Ren Chen, Viktor K. Prasanna:Accelerating Equi-Join on a CPU-FPGA Heterogeneous Platform. FCCM 2016: 212-219 45 | - Mehdi Roozmeh, Luciano Lavagno:Implementation of a performance optimized database join operation on FPGA-GPU platforms using OpenCL. NORCAS 2017: 1-6 46 | - Jared Casper, Kunle Olukotun:Hardware acceleration of database operations. FPGA 2014: 151-160 47 | - Philippos Papaphilippou, Chris Brooks, Wayne Luk:An Adaptable High-Throughput FPGA Merge Sorter for Accelerating Database Analytics. FPL 2020: 65-72 48 | - Nusrat Jahan Lisa, Annett Ungethüm, Dirk Habich, Wolfgang Lehner, Tuan D. A. Nguyen, Akash Kumar:Column Scan Acceleration in Hybrid CPU-FPGA Systems. ADMS@VLDB 2018: 22-33 49 | - René Müller, Jens Teubner, Gustavo Alonso:Sorting networks on FPGAs. VLDB J. 21(1): 1-23 (2012) 50 | 51 | 52 | ## Index 53 | 54 | - Xuan-Thuan Nguyen, Hong-Thu Nguyen, Cong-Kha Pham:An FPGA approach for fast bitmap indexing. IEICE Electron. Express 13(4): 20160006 (2016) 55 | - Xuan-Thuan Nguyen, Trong-Thuc Hoang, Hong-Thu Nguyen, Katsumi Inoue, Cong-Kha Pham:An FPGA-Based Hardware Accelerator for Energy-Efficient Bitmap Index Creation. IEEE Access 6: 16046-16059 (2018) 56 | - Xuan-Thuan Nguyen, Trong-Thuc Hoang, Hong-Thu Nguyen, Katsumi Inoue, Cong-Kha Pham:An FPGA-Based Hardware Accelerator for Energy-Efficient Bitmap Index Creation. CoRR abs/1803.11207 (2018) 57 | 58 | 59 | ## Storage manager 60 | 61 | - Satoru Watanabe, Kazuhisa Fujimoto, Yuji Saeki, Yoshifumi Fujikawa, Hiroshi Yoshino:Column-Oriented Database Acceleration Using FPGAs. ICDE 2019: 686-697 62 | - Yunhui Qiu, Jinyu Xie, Hankun Lv, Wenbo Yin, Wai-Shing Luk, Lingli Wang, Bowei Yu, Hua Chen, Xianjun Ge, Zhijian Liao, Xiaozhong Shi:FULL-KV: Flexible and Ultra-Low-Latency In-Memory Key-Value Store System Design on CPU-FPGA. IEEE Trans. Parallel Distributed Syst. 31(8): 1828-1444 (2020) 63 | - Xuan Sun, Jinghuan Yu, Zimeng Zhou, Chun Jason Xue:FPGA-based Compaction Engine for Accelerating LSM-tree Key-Value Stores. ICDE 2020: 1261-1272 64 | - Theepan Moorthy, Sathish Gopalakrishnan:IO and data management for infrastructure as a service FPGA accelerators. J. Cloud Comput. 6: 20 (2017) 65 | 66 | 67 | ## FPGA survey 68 | 69 | - Jonas Dann, Daniel Ritter, Holger memory access patterns of FPGA-based graph processing accelerators. GRADES-NDA@SIGMOD 2021: 3:1-3:10 70 | - Andreas Becher, Lekshmi B. G., David Broneske, Tobias Drewes, Bala Gurumurthy, Klaus Meyer-Wegener, Thilo Pionteck, Gunter Saake, Jürgen Teich, Stefan Wildermann:Integration of FPGAs in Database Management Systems: Challenges and Opportunities. Datenbank-Spektrum 18(3): 145-156 (2018) 71 | - Philippos Papaphilippou, Wayne Luk:Accelerating Database Systems Using FPGAs: A Survey. FPL 2018: 125-130 72 | - René Müller, Jens Teubner:FPGA: what's in it for a database? SIGMOD Conference 2009: 999-1004 73 | - Jian Fang, Yvo T. B. Mulder, Jan Hidders, Jinho Lee, H. Peter Hofstee:In-memory database acceleration on FPGAs: a survey. VLDB J. 29(1): 33-59 (2020) 74 | - Bharat Sukhwani, Hong Min, Mathew Thoennes, Parijat Dube, Balakrishna Iyer, Bernard Brezzo, Donna Dillenberger, Sameh W. Asaad:Database analytics acceleration using FPGAs. PACT 2012: 411-420 75 | - Jian Fang:Database Acceleration on FPGAs. Delft University of Technology, Netherlands, 2019 76 | - Kaspar Mätas, Dirk Koch:Transparent Integration of a Dynamic FPGA Database Acceleration System. FPL 2020: 365-366 77 | 78 | 79 | ## Concurrent execution 80 | 81 | - Muhsen Owaida, Gustavo Alonso, Laura Fogliarini, Anthony Hock-Koon, Pierre-Etienne Melet:Lowering the Latency of Data Processing Pipelines Through FPGA based Hardware Acceleration. Proc. VLDB Endow. 13(1): 71-85 (2019) 82 | - Kaan Kara, Jana Giceva, Gustavo Alonso:FPGA-based Data Partitioning. SIGMOD Conference 2017: 433-445 83 | - Ze-ke Wang, Bingsheng He, Wei Zhang:Improving Data Partitioning Performance on OpenCL-Based FPGAs. FCCM 2015: 34 84 | - Ze-ke Wang, Bingsheng He, Wei Zhang:A study of data partitioning on OpenCL-based FPGAs. FPL 2015: 1-8 85 | - Malte Vesper:Dynamic stream processing pipelines on FPGAs targeting database acceleration. University of Manchester, UK, 2019 86 | - Umar Ibrahim Minhas, Roger F. Woods, Dimitrios S. Nikolopoulos, Georgios Karakonstantis:Efficient, Dynamic Multi-Task Execution on FPGA-Based Computing Systems. IEEE Trans. Parallel Distributed Syst. 33(3): 710-722 (2022) 87 | - Ze-ke Wang, Johns Paul, Bingsheng He, Wei Zhang:Multikernel Data Partitioning With Channel on OpenCL-Based FPGAs. IEEE Trans. Very Large Scale Integr. Syst. 25(6): 1906-1918 (2017) 88 | 89 | ## Query compilation 90 | 91 | - René Müller, Jens Teubner, Gustavo Alonso:Streams on Wires - A Query Compiler for FPGAs. Proc. VLDB Endow. 2(1): 229-240 (2009) 92 | -------------------------------------------------------------------------------- /Paper/GDBMS_PaperList.md: -------------------------------------------------------------------------------- 1 | ## GPU Database System 2 | 3 | + Rubao Lee, Minghong Zhou, Chi Li, Shenggang Hu, Jianping Teng, Dongyang Li, Xiaodong Zhang:The Art of Balance: A RateupDB Experience of Building a CPU/GPU Hybrid Database Product. Proc. VLDB Endow. 14(12): 2999-3013 (2021) 4 | 5 | ## GDBMS Survey 6 | 7 | + 裴威,李战怀,潘巍.GPU 数据库核心技术综述.软件学报,2021,32(3):859−885. 8 | + Pei W, Li ZH, Pan W. Survey of key technologies in GPU database system. Ruan Jian Xue Bao/Journal of Software, 2021,32(3):859−885 (in Chinese). 9 | + Johns Paul, Bingsheng He, Shengliang Lu, Chiew Tong Lau:Revisiting hash join on graphics processors: a decade later. Distributed Parallel Databases 38(4): 771-793 (2020) 10 | + Iya Arefyeva, David Broneske, Gabriel Campero Durand, Marcus Pinnecke, Gunter Saake:Memory Management Strategies in CPU/GPU Database Systems: A Survey. BDAS 2018: 128-142 11 | + Brenton Lessley, Hank Childs:Data-Parallel Hashing Techniques for GPU Architectures. IEEE Trans. Parallel Distributed Syst. 31(1): 237-250 (2020) 12 | + Makoto Yabuta, Anh Nguyen, Shinpei Kato, Masato Edahiro, Hideyuki Kawashima:Relational Joins on GPUs: A Closer Look. IEEE Trans. Parallel Distributed Syst. 28(9): 2663-2673 (2017) 13 | + Sparsh Mittal:A Survey of Techniques for Architecting and Managing GPU Register File. IEEE Trans. Parallel Distributed Syst. 28(1): 16-28 (2017) 14 | + Cagri Balkesen, Gustavo Alonso, Jens Teubner, M. Tamer Özsu:Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited. Proc. VLDB Endow. 7(1): 85-96 (2013) 15 | + Ran Rui, Hao Li, Yi-Cheng Tu:Join algorithms on GPUs: A revisit after seven years. IEEE BigData 2015: 2541-2550 16 | + Jiong He, Mian Lu, Bingsheng He:Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture. Proc. VLDB Endow. 6(10): 889-900 (2013) 17 | + Wenbin Fang, Bingsheng He, Qiong Luo:Database Compression on Graphics Processors. Proc. VLDB Endow. 3(1): 670-680 (2010) 18 | + Daniel J. Abadi, Daniel S. Myers, David J. DeWitt, Samuel Madden:Materialization Strategies in a Column-Oriented DBMS. ICDE 2007: 466-475 19 | + Tim Kaldewey, Guy M. Lohman, René Müller, Peter Benjamin Volk:GPU join processing revisited. DaMoN 2012: 55-62 20 | + Stratos Idreos, Fabian Groffen, Niels Nes, Stefan Manegold, K. Sjoerd Mullender, Martin L. Kersten:MonetDB: Two Decades of Research in Column-oriented Database Architectures. IEEE Data Eng. Bull. 35(1): 40-45 (2012) 21 | + Sebastian Breß, Max Heimel, Norbert Siegmund, Ladjel Bellatreche, Gunter Saake:GPU-Accelerated Database Systems: Survey and Open Challenges. Trans. Large Scale Data Knowl. Centered Syst. 15: 1-35 (2014) 22 | + Yi-Cheng Tu, Anand Kumar, Di Yu, Ran Rui, Ryan Wheeler:Data management systems on GPUs: promises and challenges. SSDBM 2013: 33:1-33:4 23 | 24 | ## Query Engine 25 | 26 | + Periklis Chrysogelos, Panagiotis Sioulas, Anastasia Ailamaki:Hardware-conscious Query Processing in GPU-accelerated Analytical Engines. CIDR 2019 27 | 28 | ## Query execution 29 | 30 | + Sofoklis Floratos, Mengbai Xiao, Hao Wang, Chengxin Guo, Yuan Yuan, Rubao Lee, Xiaodong Zhang:NestGPU: Nested Query Processing on GPU. ICDE 2021: 1008-1019 31 | + Harish Doraiswamy, Juliana Freire:A GPU-friendly Geometric Data Model and Algebra for Spatial Queries. SIGMOD Conference 2020: 1875-1885 32 | + Ihab F. Ilyas, George Beskales, Mohamed A. Soliman:A survey of top-k query processing techniques in relational database systems.ACM Comput. Surv. 40(4): 11:1-11:58 (2008) 33 | + Anil Shanbhag, Holger Pirk, Samuel Madden:Efficient Top-K Query Processing on Massively Parallel Hardware.SIGMOD Conference 2018: 1557-1570 34 | + Muhammad A. Awad, Saman Ashkiani, Rob Johnson, Martin Farach-Colton, John D. Owens: 35 | Engineering a high-performance GPU B-Tree. PPoPP 2019: 145-157 36 | + Dalin Wang, Feng Zhang, Weitao Wan, Hourun Li, Xiaoyong Du:FineQuery: Fine-Grained Query Processing on CPU-GPU Integrated Architectures. CLUSTER 2021: 355-365 37 | + Zhila Nouri Lewis, Yi-Cheng Tu:G-PICS: A Framework for GPU-Based Spatial Indexing and Query Processing.IEEE Trans. Knowl. Data Eng. 34(3): 1243-1257 (2022) 38 | + Mitchell Nelson, Zachary Sorenson, Joseph M. Myre, Jason Sawin, David Chiu:GPU Acceleration of Range Queries over Large Data Sets. BDCAT 2019: 11-20 39 | + Dariusz Rafal Augustyn, Lukasz Warchal:GPU-Accelerated Query Selectivity Estimation Based on Data Clustering and Monte Carlo Integration Method Developed in CUDA Environment. ADBIS (2) 2013: 215-224 40 | + Yansong Zhang, Yu Zhang, Jiaheng Lu, Shan Wang, Zhuan Liu, Ruichen Han:One size does not fit all: accelerating OLAP workloads with GPUs. Distributed Parallel Databases 38(4): 995-1037 (2020) 41 | + Bingsheng He, Mian Lu, Ke Yang, Rui Fang, Naga K. Govindaraju, Qiong Luo, Pedro V. Sander:Relational query coprocessing on graphics processors. ACM Trans. Database Syst. 34(4): 21:1-21:39 (2009) 42 | + Max Heimel, Martin Kiefer, Volker Markl:Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation. SIGMOD Conference 2015: 1477-1492 43 | + Sina Meraji, John Keenleyside, Sunil Kamath, Bob Blainey:Towards a Combined Grouping and Aggregation Algorithm for Fast Query Processing in Columnar Databases with GPUs. IPDPS Workshops 2015: 594-603 44 | + Hongzhi Wang, Zheng Wang, Ning Li, Xinxin Kong:Efficient OLAP algorithms on GPU-accelerated Hadoop clusters. Distributed Parallel Databases 37(4): 507-542 (2019) 45 | + Aunn Raza, Periklis Chrysogelos, Panagiotis Sioulas, Vladimir Indjic, Angelos-Christos G. Anadiotis, Anastasia Ailamaki:GPU-accelerated data management under the test of time. CIDR 2020 46 | + Yuan Yuan, Rubao Lee, Xiaodong Zhang:The Yin and Yang of Processing Data Warehousing Queries on GPU Devices. Proc. VLDB Endow. 6(10): 817-828 (2013) 47 | + Henning Funke, Jens Teubner:Data-Parallel Query Processing on Non-Uniform Data. Proc. VLDB Endow. 13(6): 884-897 (2020) 48 | + Bingsheng He, Mian Lu, Ke Yang, Rui Fang, Naga K. Govindaraju, Qiong Luo, Pedro V. Sander: 49 | Relational query coprocessing on graphics processors. ACM Trans. Database Syst. 34(4): 21:1-21:39 (2009) 50 | + Rui Fang, Bingsheng He, Mian Lu, Ke Yang, Naga K. Govindaraju, Qiong Luo, Pedro V. Sander:GPUQP: query co-processing using graphics processors. SIGMOD Conference 2007: 1061-1063 51 | + Xuntao Cheng, Bingsheng He, Mian Lu, Chiew Tong Lau, Huynh Phung Huynh, Rick Siow Mong Goh:Efficient Query Processing on Many-core Architectures: A Case Study with Intel Xeon Phi Processor. SIGMOD Conference 2016: 2081-2084 52 | + Shuhao Zhang, Jiong He, Bingsheng He, Mian Lu:OmniDB: Towards Portable and Efficient Query Processing on Parallel CPU/GPU Architectures. Proc. VLDB Endow. 6(12): 1374-1377 (2013) 53 | 54 | ## Query compilation 55 | 56 | + Ruby Y. Tahboub, Tiark Rompf:Architecting a Query Compiler for Spatial Workloads. SIGMOD Conference 2020: 2103-2118 57 | 58 | 59 | 60 | ## Query Optimization 61 | 62 | + Alexander Krolik, Clark Verbrugge, Laurie J. Hendren:r3d3: Optimized Query Compilation on GPUs. CGO 2021: 277-288 63 | + Xue-Xuan Hu, Jianqing Xi, De-You Tang:Optimization for Multi-Join Queries on the GPU. IEEE Access 8: 118380-118395 (2020) 64 | + Mehrad Eslami, Vahid Mahmoodian, Iman Dayarian, Hadi Charkhgard, Yicheng Tu:Query batching optimization in database systems. Comput. Oper. Res. 121: 104983 (2020) 65 | + Viktor Leis:Query Processing and Optimization in Modern Database Systems. BTW 2017: 507-516 66 | + Jun Hyung Shin, Florin Rusu, Alex Suhan:Exact Selectivity Computation for Modern In-Memory Database Query Optimization. CoRR abs/1901.01488 (2019) 67 | + Tsuyoshi Tanaka, Hiroshi Ishikawa:Measurement-Based Cost Calculation Method Focusing on CPU Architecture for Database Query Optimization. MEDES 2019: 56-65 68 | + Thuan Tan Nguyen, Ban Van Doan, Chau Ngoc Truong, Trinh Thi Thuy Tran:Clustering and Query Optimization in Fuzzy Object-Oriented Database. Int. J. Nat. Comput. Res. 8(1): 1-17 (2019) 69 | + Eduardo Mella, M. Andrea Rodríguez, Loreto Bravo, Diego Gatica:Query rewriting for semantic query optimization in spatial databases. GeoInformatica 23(1): 79-104 (2019) 70 | + Vahideh Panahi, Nima Jafari Navimipour:Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators. Concurr. Comput. Pract. Exp. 31(17) (2019) 71 | + Xiang Dong, Lijia Zeng:Research on Query Optimization of Classic Art Database Based on Artificial Intelligence and Edge Computing. Wirel. Commun. Mob. Comput. 2021: 6118113:1-6118113:11 (2021) 72 | + Deepak Kumar, Vijay Kumar Jha:An improved query optimization process in big data using ACO-GA algorithm and HDFS map reduce technique. Distributed Parallel Databases 39(1): 79-96 (2021) 73 | + Kristian F. D. Rietveld, Harry A. G. Wijshoff:Redefining The Query Optimization Process. CoRR abs/2203.01079 (2022) 74 | + Matteo Cordioli:Multi-Objective Optimization, different approach to query a database. CoRR abs/2202.01550 (2022) 75 | + Jan Kossmann, Thorsten Papenbrock, Felix Naumann:Data dependencies for query optimization: a survey. VLDB J. 31(1): 1-22 (2022) 76 | + Bingxu Zheng, Xiang Li, Zhenzhen Tian, Limin Meng:Optimization Method for Distributed Database Query Based on an Adaptive Double Entropy Genetic Algorithm. IEEE Access 10: 4640-4648 (2022) 77 | + Alexander Krolik, Clark Verbrugge, Laurie J. Hendren:r3d3: Optimized Query Compilation on GPUs. CGO 2021: 277-288 78 | + Cheng Chen, Jun Yang, Mian Lu, Taize Wang, Zhao Zheng, Yuqiang Chen, Wenyuan Dai, Bingsheng He, Weng-Fai Wong, Guoan Wu, Yuping Zhao, Andy Rudoff:Optimizing An In-memory Database System For AI-powered On-line Decision Augmentation Using Persistent Memory. Proc. VLDB Endow. 14(5): 799-812 (2021) 79 | 80 | 81 | ## Storage Manager 82 | 83 | + Daniel J. Abadi, Samuel Madden, Nabil Hachem:Column-stores vs. row-stores: how different are they really? SIGMOD Conference 2008: 967-980 84 | + Alexandra Angerd, Erik Sintorn, Per Stenström:A GPU Register File using Static Data Compression. ICPP 2020: 59:1-59:10 85 | + Kai Zhang, Jiayu Hu, Bingsheng He, Bei Hua:DIDO: Dynamic Pipelines for In-Memory Key-Value Stores on Coupled CPU-GPU Architectures. ICDE 2017: 671-682 86 | + Daniel J. Abadi, Samuel Madden, Miguel Ferreira:Integrating compression and execution in column-oriented database systems. SIGMOD Conference 2006: 671-682 87 | + Won Gi Choi, Doyoung Kim, Hongchan Roh, Sanghyun Park:OurRocks: Offloading Disk Scan Directly to GPU in Write-Optimized Database System. IEEE Trans. Computers 70(11): 1831-1844 (2021) 88 | + Sebastian Breß:The Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS. Datenbank-Spektrum 14(3): 199-209 (2014) 89 | + Yi-Cheng Tu, Anand Kumar, Di Yu, Ran Rui, Ryan Wheeler:Data management systems on GPUs: promises and challenges. SSDBM 2013: 33:1-33:4 90 | + Anil Shanbhag, Samuel Madden, Xiangyao Yu:A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics. SIGMOD Conference 2020: 1617-1632 91 | + Kai Zhang, Kaibo Wang, Yuan Yuan, Lei Guo, Rubao Lee, Xiaodong Zhang:Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores. Proc. VLDB Endow. 8(11): 1226-1237 (2015) 92 | 93 | ## index 94 | 95 | + Harish Doraiswamy, Huy T. Vo, Cláudio T. Silva, Juliana Freire:A GPU-based index to support interactive spatio-temporal queries over historical data. ICDE 2016: 1086-1097 96 | + Mincheol Kim, Ling Liu, Wonik Choi:A GPU-Aware Parallel Index for Processing High-Dimensional Big Data. IEEE Trans. Computers 67(10): 1388-1402 (2018) 97 | + Amirhesam Shahvarani, Hans-Arno Jacobsen:A Hybrid B+-tree as Solution for In-Memory Indexing on CPU-GPU Heterogeneous Computing Platforms. SIGMOD Conference 2016: 1523-1538 98 | + Jinwoong Kim, Sumin Hong, Beomseok Nam:A Performance Study of Traversing Spatial Indexing Structures in Parallel on GPU. HPCC-ICESS 2012: 855-860 99 | + Muhammad A. Awad, Saman Ashkiani, Serban D. Porumbescu, Martin Farach-Colton, John D. Owens:Better GPU Hash Tables. CoRR abs/2108.07232 (2021) 100 | + Chee Yong Chan, Yannis E. Ioannidis:Bitmap Index Design and Evaluation. SIGMOD Conference 1998: 355-366 101 | + Haoyu Cheng, Yong Zhang, Yun Xu:BitMapper2: A GPU-Accelerated All-Mapper Based on the Sparse q-Gram Index. IEEE ACM Trans. Comput. Biol. Bioinform. 16(3): 886-897 (2019) 102 | + Martin Krulis, Hasmik Osipyan, Stéphane Marchand-Maillet:Employing GPU architectures for permutation-based indexing. Multim. Tools Appl. 76(9): 11859-11887 (2017) 103 | + Changkyu Kim, Jatin Chhugani, Nadathur Satish, Eric Sedlar, Anthony D. Nguyen, Tim Kaldewey, Victor W. Lee, Scott A. Brandt, Pradeep Dubey:FAST: fast architecture sensitive tree search on modern CPUs and GPUs. SIGMOD Conference 2010: 339-350 104 | + Lianyin Jia, Yongbin Zhang, Mengjuan Li, Jiaman Ding, Jinguo You:GPU Based Hash Segmentation Index for Fast T-overlap Query. ICPCSEE (1) 2017: 39-51 105 | + Zhila Nouri, Yi-Cheng Tu:GPU-based parallel indexing for concurrent spatial query processing. SSDBM 2018: 23:1-23:12 106 | + Santosh Pandey, Xiaoye Sherry Li, Aydin Buluç, Jiejun Xu, Hang Liu:H-INDEX: Hash-Indexing for Parallel Triangle Counting on GPUs. HPEC 2019: 1-7 107 | + Lukás Polok, Pavel Smrz:Implementing random indexing on GPU. SpringSim (HPC) 2011: 134-142 108 | + Francesco Fusco, Michail Vlachos, Xenofontas A. Dimitropoulos, Luca Deri:Indexing million of packets per second using GPUs. Internet Measurement Conference 2013: 327-332 109 | + Loukas Bampis, Chryssanthi Iakovidou, Savvas A. Chatzichristofis, Yiannis S. Boutalis, Angelos Amanatiadis:Real-time indexing for large image databases: color and edge directivity descriptor on GPU. J. Supercomput. 71(3): 909-937 (2015) 110 | + Ali Hadian, Thomas Heinis:Shift-Table: A Low-latency Learned Index for Range Queries using Model Correction. EDBT 2021: 253-264 111 | + Botao Peng, Panagiota Fatourou, Themis Palpanas:SING: Sequence Indexing Using GPUs. ICDE 2021: 1883-1888 112 | + Kesheng Wu, Wendy S. Koegler, Jacqueline Chen, Arie Shoshani:Using Bitmap Index for Interactive Exploration of Large Datasets. SSDBM 2003: 65-74 113 | + Holger Pirk, Stefan Manegold, Martin L. Kersten:Accelerating Foreign-Key Joins using Asymmetric Memory Channels. ADMS@VLDB 2011: 27-35 114 | + Anas Abu-Doleh, Erik Saule, Kamer Kaya, Ümit V. Çatalyürek:Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs. BCB 2013: 341 115 | 116 | ## join 117 | 118 | + Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga K. Govindaraju, Qiong Luo, Pedro V. Sander:Relational joins on graphics processors. SIGMOD Conference 2008: 511-524 119 | + Sven Winkelhaus, Minqi Zhang, Eric H. Grosse, Christoph H. Glock:Hybrid order picking: A simulation model of a joint manual and autonomous order picking system. Comput. Ind. Eng. 167: 107981 (2022) 120 | 121 | ## Query model 122 | 123 | - Simon Zhang, Mengbai Xiao, Chengxin Guo, Liang Geng, Hao Wang, Xiaodong Zhang:HYPHA: a framework based on separation of parallelisms to accelerate persistent homology matrix reduction. ICS 2019: 69-81 124 | - Christos Bellas, Anastasios Gounaris:HySet: A hybrid framework for exact set similarity join using a GPU. Parallel Comput. 104-105: 102790 (2021) 125 | - Raja Appuswamy, Manos Karpathiotakis, Danica Porobic, Anastasia Ailamaki:The Case For Heterogeneous HTAP. CIDR 2017 126 | - Zhifei Pang, Sai Wu, Haichao Huang, Zhouzhenyan Hong, Yuqing Xie:AQUA+: Query Optimization for Hybrid Database-MapReduce System. Knowl. Inf. Syst. 63(4): 905-938 (2021) 127 | 128 | ## Operator operation 129 | 130 | - Chengxin Guo, Hong Chen, Cuiping Li, Tianzhen Wu:A Memory Access Reduced Sort on Multi-core GPU. HPCC/SmartCity/DSS 2018: 586-593 131 | - Kevin Angstadt, Ed Harcourt:A virtual machine model for accelerating relational database joins using a general purpose GPU. SpringSim (HPS) 2015: 127-134 132 | - Lining Yu, Tiezheng Nie, Derong Shen, Yue Kou:Accelerating Progressive Set Similarity Join with the CPU-GPU Architecture. Big Data Res. 26: 100267 (2021) 133 | - Chengxin Guo, Hong Chen, Feng Zhang, Cuiping Li:Distributed Join Algorithms on Multi-CPU Clusters with GPUDirect RDMA. ICPP 2019: 65:1-65:10 134 | - Saman Ashkiani, Shengren Li, Martin Farach-Colton, Nina Amenta, John D. Owens:GPU LSM: A Dynamic Dictionary Data Structure for the GPU. IPDPS 2018: 430-440 135 | - Dariusz Rafal Augustyn, Lukasz Warchal:GPU-Accelerated Method of Query Selectivity Estimation for Non Equi-Join Conditions Based on Discrete Fourier Transform. ADBIS (2) 2014: 215-227 136 | - Naga K. Govindaraju, Jim Gray, Ritesh Kumar, Dinesh Manocha:GPUTeraSort: high performance graphics co-processor sorting for large database management. SIGMOD Conference 2006: 325-336 137 | - Chengxin Guo, Hong Chen, Feng Zhang, Cuiping Li:Parallel Hybrid Join Algorithm on GPU. HPCC/SmartCity/DSS 2019: 1572-1579 138 | - Makoto Yabuta, Anh Nguyen, Shinpei Kato, Masato Edahiro, Hideyuki Kawashima:Relational Joins on GPUs: A Closer Look. IEEE Trans. Parallel Distributed Syst. 28(9): 2663-2673 (2017) 139 | - Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga K. Govindaraju, Qiong Luo, Pedro V. Sander:Relational joins on graphics processors. SIGMOD Conference 2008: 511-524 140 | - Jiong He, Mian Lu, Bingsheng He:Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture. Proc. VLDB Endow. 6(10): 889-900 (2013) 141 | - Amirhesam Shahvarani, Hans-Arno Jacobsen:Parallel Index-based Stream Join on a Multicore CPU. SIGMOD Conference 2020: 2523-2537 142 | - Evangelia A. Sitaridi, Kenneth A. Ross:Ameliorating memory contention of OLAP operators on GPU processors. DaMoN 2012: 39-47 143 | - Ran Rui, Yi-Cheng Tu:Fast Equi-Join Algorithms on GPUs: Design and Implementation. SSDBM 2017: 17:1-17:12 144 | - Bingsheng He, Naga K. Govindaraju, Qiong Luo, Burton Smith:Efficient gather and scatter operations on graphics processors. SC 2007: 46 145 | - Dmitri I. Arkhipov, Di Wu, Keqin Li, Amelia C. Regan:Sorting with GPUs: A Survey. CoRR abs/1709.02520 (2017) 146 | - Johns Paul, Shengliang Lu, Bingsheng He, Chiew Tong Lau:MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures. SIGMOD Conference 2021: 1413-1425 147 | - Chengxin Guo, Hong Chen, Feng Zhang, Cuiping Li:Distributed Join Algorithms on Multi-CPU Clusters with GPUDirect RDMA. ICPP 2019: 65:1-65:10 148 | - Hao Gao, Nikolai Sakharnykh:Scaling Joins to a Thousand GPUs. ADMS@VLDB 2021: 55-64 149 | - Panagiotis Sioulas, Periklis Chrysogelos, Manos Karpathiotakis, Raja Appuswamy, Anastasia Ailamaki:Hardware-Conscious Hash-Joins on GPUs. ICDE 2019: 698-709 150 | - Ran Rui, Yi-Cheng Tu:Fast Equi-Join Algorithms on GPUs: Design and Implementation. SSDBM 2017: 17:1-17:12 151 | - Jiong He, Mian Lu, Bingsheng He: 152 | Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture. Proc. VLDB Endow. 6(10): 889-900 (2013) 153 | - Bingsheng He, Ke Yang, Rui Fang, Mian Lu, Naga K. Govindaraju, Qiong Luo, Pedro V. Sander:Relational joins on graphics processors. SIGMOD Conference 2008: 511-524 154 | - Ran Rui, Hao Li, Yi-Cheng Tu:Join algorithms on GPUs: A revisit after seven years. IEEE BigData 2015: 2541-2550 155 | - Tim Kaldewey, Guy M. Lohman, René Müller, Peter Benjamin Volk:GPU join processing revisited. DaMoN 2012: 55-62 156 | 157 | ## Cross device transmission 158 | 159 | - Michael Gowanlock, Zane Fink, Ben Karsin, Jordan Wright:A study of work distribution and contention in database primitives on heterogeneous CPU/GPU architectures. SAC 2021: 311-320 160 | - Ang Li, Shuaiwen Leon Song, Jieyang Chen, Jiajia Li, Xu Liu, Nathan R. Tallent, Kevin J. Barker:Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect. IEEE Trans. Parallel Distributed Syst. 31(1): 94-110 (2020) 161 | - Denis Foley, John Danskin:Ultra-Performance Pascal GPU and NVLink Interconnect. IEEE Micro 37(2): 7-17 (2017) 162 | - Chen Li, Jun Yang, Yifan Sun, Lingling Jin, Lingjie Xu, Zheng Cao, Pengfei Fan, David R. Kaeli, Sheng Ma, Yang Guo:Priority-Based PCIe Scheduling for Multi-Tenant Multi-GPU Systems. IEEE Comput. Archit. Lett. 18(2): 157-160 (2019) 163 | - Daniele Buono, Fausto Artico, Fabio Checconi, Jee W. Choi, Xinyu Que, Lars Schneidenbach:Data Analytics with NVLink: An SpMV Case Study. Conf. Computing Frontiers 2017: 89-96 164 | - Zhihao Jia, Yongkee Kwon, Galen M. Shipman, Patrick S. McCormick, Mattan Erez, Alex Aiken:A Distributed Multi-GPU System for Fast Graph Processing. Proc. VLDB Endow. 11(3): 297-310 (2017) 165 | - Orestis Polychroniou, Rajkumar Sen, Kenneth A. Ross:Track join: distributed joins with minimal network traffic. SIGMOD Conference 2014: 1483-1494 166 | 167 | ## data compression 168 | 169 | - Nandita Vijaykumar, Gennady Pekhimenko, Adwait Jog, Abhishek Bhowmick, Rachata Ausavarungnirun, Chita R. Das, Mahmut T. Kandemir, Todd C. Mowry, Onur Mutlu:A case for core-assisted bottleneck acceleration in GPUs: enabling flexible data compression with assist warps. ISCA 2015: 41-53 170 | - Sparsh Mittal, Jeffrey S. Vetter:A Survey Of Architectural Approaches for Data Compression in Cache and Main Memory Systems. IEEE Trans. Parallel Distributed Syst. 27(5): 1524-1536 (2016) 171 | - Wenbin Fang, Bingsheng He, Qiong Luo:Database Compression on Graphics Processors. Proc. VLDB Endow. 3(1): 670-680 (2010) 172 | - Sangpil Lee, Keunsoo Kim, Gunjae Koo, Hyeran Jeon, Murali Annavaram, Won Woo Ro:Improving Energy Efficiency of GPUs through Data Compression and Compressed Execution. IEEE Trans. Computers 66(5): 834-847 (2017) 173 | - Sangpil Lee, Keunsoo Kim, Gunjae Koo, Hyeran Jeon, Won Woo Ro, Murali Annavaram:Warped-compression: enabling power efficient GPUs through register compression. ISCA 2015: 502-514 174 | 175 | ## Concurrent execution 176 | 177 | - Hongwen Dai, Zhen Lin, Chao Li, Chen Zhao, Fei Wang, Nanning Zheng, Huiyang Zhou:Accelerate GPU Concurrent Kernel Execution by Mitigating Memory Pipeline Stalls. HPCA 2018: 208-220 178 | - Kaibo Wang, Kai Zhang, Yuan Yuan, Siyuan Ma, Rubao Lee, Xiaoning Ding, Xiaodong Zhang:Concurrent Analytical Query Processing with GPUs. Proc. VLDB Endow. 7(11): 1011-1022 (2014) 179 | - Brenton Lessley, Hank Childs:Data-Parallel Hashing Techniques for GPU Architectures. IEEE Trans. Parallel Distributed Syst. 31(1): 237-250 (2020) 180 | - Qing-Feng Zhang, Shan-Shan Li, Jing Xu:QScheduler: A Tool for Parallel Query Processing in Database Systems. ICECCS 2014: 73-76 181 | - Simin You, Jianting Zhang, Le Gruenwald:Parallel spatial query processing on GPUs using R-trees. BigSpatial@SIGSPATIAL 2013: 23-31 182 | - Sebastian Breß, Norbert Siegmund, Max Heimel, Michael Saecker, Tobias Lauer, Ladjel Bellatreche, Gunter Saake:Load-aware inter-co-processor parallelism in database query processing. Data Knowl. Eng. 93: 60-79 (2014) -------------------------------------------------------------------------------- /Paper/MPP_PaperList.md: -------------------------------------------------------------------------------- 1 | ## Massively Parallel Processing(MPP) 2 | 3 | 4 | ### Survey 5 | + Nikolay Golov, Lars Rönnbäck:Big Data normalization for massively parallel processing databases. Comput. Stand. Interfaces 54: 86-93 (2017) 6 | + Dan Chen, Yangyang Hu, Chang Cai, Ke Zeng, Xiaoli Li:Brain big data processing with massively parallel computing technology: challenges and opportunities. Softw. Pract. Exp. 47(3): 405-420 (2017) 7 | + Frank Meinl, Martin Kunert, Holger Blume:Massively parallel signal processing challenges within a driver assistant prototype framework first case study results with a novel MIMO-radar. ICSAMOS 2014: 351-357 8 | + Kevin B. Daly, Jay B. Benziger, Pablo G. Debenedetti, Athanassios Z. Panagiotopoulos:Massively parallel chemical potential calculation on graphics processing units. Comput. Phys. Commun. 183(10): 2054-2062 (2012) 9 | 10 | 11 | ### Architecture 12 | + Yeseong Kim, Mohsen Imani, Saransh Gupta, Minxuan Zhou, Tajana Simunic Rosing:Massively Parallel Big Data Classification on a Programmable Processing In-Memory Architecture. ICCAD 2021: 1-9 2020 13 | + Alexander Schöch, Carlo Bach, Andreas Ettemeyer, Sabine Linz-Dittrich:GePaRDT: a framework for massively parallel processing of dataflow graphs. Real-Time Image and Video Processing 2012: 843702 14 | 15 | 16 | ### Process 17 | + Yongmin Hu, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, Depei Qian:Massively Scaling Seismic Processing on Sunway TaihuLight Supercomputer. IEEE Trans. Parallel Distributed Syst. 31(5): 1194-1208 (2020) 18 | + Christopher D. Rickett, Kristyn J. Maschhoff, Sreenivas R. Sukumar:Massively Parallel Processing Database for Sequence and Graph Data Structures Applied to Rapid-Response Drug Repurposing. IEEE BigData 2020: 2967-2976 19 | + Shilpa Mayannavar, Uday Wali:Design of Reconfigurable Multi-Operand Adder for Massively Parallel Processing. CoRR abs/2008.02584 (2020)2019 20 | + Michael Kenzel, Bernhard Kerbl, Wolfgang Tatzgern, Elena Ivanchenko, Dieter Schmalstieg, Markus Steinberger:On-the-fly Vertex Reuse for Massively-Parallel Software Geometry Processing. Proc. ACM Comput. Graph. Interact. Tech. 1(2): 28:1-28:17 (2018) 21 | + Vasileios Zois, Divya Gupta, Vassilis J. Tsotras, Walid A. Najjar, Jean-François Roy:Massively parallel skyline computation for processing-in-memory architectures. PACT 2018: 1:1-1:12 22 | + Wonyong Sung, Dong-hwan Lee, Kyuyeon Hwang:Massively parallel computation of linear recurrence equations with graphics processing units. SAMOS 2018: 10-17 23 | + Marcin Sylwestrzak, Daniel Szlag, Paul J. Marchand, Ashwin S. Kumar, Theo Lasser:Massively parallel data processing for quantitative total flow imaging with optical coherence microscopy and tomography. Comput. Phys. Commun. 217: 128-137 (2017) 24 | + Christopher Husmann, Georgios Georgis, Konstantinos Nikitopoulos, Kyle Jamieson:FlexCore: Massively Parallel and Flexible Processing for Large MIMO Access Points. NSDI 2017: 197-211 25 | + Xavier J. A. Bellekens, Christos Tachtatzis, Robert C. Atkinson, Craig Renfrew, Tony Kirkham:GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing. CoRR abs/1704.02278 (2017) 26 | + Xavier J. A. Bellekens, Christos Tachtatzis, Robert C. Atkinson, Craig Renfrew, Tony Kirkham:GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing. SIN 2014: 295 27 | + Bo Li, János Sallai, Péter Völgyesi, Ákos Lédeczi:Rapid prototyping of image processing workflows on massively parallel architectures. WISES 2012: 15-20 28 | 29 | 30 | ### Engine 31 | + Li Su, Yongluan Zhou:Passive and Partially Active Fault Tolerance for Massively Parallel Stream Processing Engines. IEEE Trans. Knowl. Data Eng. 31(1): 32-45 (2019) 32 | + Li Su, Yongluan Zhou:Tolerating correlated failures in Massively Parallel Stream Processing Engines. ICDE 2016: 517-528 2015 33 | + Kasper Grud Skat Madsen, Yongluan Zhou:Dynamic Resource Management In a Massively Parallel Stream Processing Engine. CIKM 2015: 13-22 34 | + Li Su, Yongluan Zhou:Passive and Partially Active Fault Tolerance for Massively Parallel Stream Processing Engines. CoRR abs/1508.04907 (2015)2014 35 | + Lei Chang, Zhanwei Wang, Tao Ma, Lirong Jian, Lili Ma, Alon Goldshuv, Luke Lonergan, Jeffrey Cohen, Caleb Welton, Gavin Sherry, Milind Bhandarkar:HAWQ: a massively parallel processing SQL engine in hadoop. SIGMOD Conference 2014: 1223-1234 36 | + Dan Chen, Lizhe Wang, Gaoxiang Ouyang, Xiaoli Li:Massively Parallel Neural Signal Processing on a Many-Core Platform. Comput. Sci. Eng. 13(6): 42-51 (2011) 37 | 38 | 39 | ### Algorithm 40 | + Paul Ferré:Algorithm-architecture adequacy of spiking neural networks for massively parallel processing hardware. (Adéquation algorithme-architecture de réseaux de neurones à spikes pour les architectures matérielles massivement parallèles). Paul Sabatier University, Toulouse, France, 2018 41 | + Anil Shanbhag, Holger Pirk, Samuel Madden:Efficient Top-K Query Processing on Massively Parallel Hardware. SIGMOD Conference 2018: 1557-1570 42 | + Waqar Hussain, Roberto Airoldi, Henry Hoffmann, Tapani Ahonen, Jari Nurmi:HARP2: An X-Scale Reconfigurable Accelerator-Rich Platform for Massively-Parallel Signal Processing Algorithms. J. Signal Process. Syst. 85(3): 341-353 (2016) 43 | + Man Leung Wong, Geng Cui:Data Mining Using Parallel Multi-objective Evolutionary Algorithms on Graphics Processing Units. Massively Parallel Evolutionary Computation on GPGPUs 2013: 287-307 2012 44 | 45 | 46 | ### Performance 47 | + Mikhail Chernoskutov:Accelerating Processing of Scale-Free Graphs on Massively-Parallel Architectures. ICA3PP 2017: 759-765 48 | + Abhishek Roy, Yanlei Diao, Uday Evani, Avinash Abhyankar, Clinton Howarth, Rémi Le Priol, Toby Bloom:Massively Parallel Processing of Whole Genome Sequence Data: An In-Depth Performance Study. SIGMOD Conference 2017: 187-202 49 | + Keiichiro Fukazawa, Takeshi Nanri:Performance of Large Scale MHD Simulation of Global Planetary Magnetosphere with Massively Parallel Scalar Type Supercomputer Including Post Processing. HPCC-ICESS 2012: 976-982 50 | + Alejandro Nieto, Víctor M. Brea, David López Vilariño, Roberto R. Osorio:Performance analysis of massively parallel embedded hardware architectures for retinal image processing. EURASIP J. Image Video Process. 2011: 10 (2011) 51 | + Wenwu Tang, Wenpeng Feng, Meijuan Jia:Massively parallel spatial point pattern analysis: Ripley's K function accelerated using graphics processing units. Int. J. Geogr. Inf. Sci. 29(3): 412-439 (2015) 52 | 53 | 54 | ### Model 55 | + Ralf Östermark:Massively parallel processing of recursive multi-period portfolio models. Eur. J. Oper. Res. 259(1): 344-366 (2017) 56 | + Qingsong Guo, Yongluan Zhou:CBP: A New Parallelization Paradigm for Massively Distributed Stream Processing. DASFAA (2) 2017: 304-320 57 | + Amir Said, Ton Kalker, Bowon Lee, Majid Fozunbal:Massively parallel processing of signals in dense microphone arrays. ISCAS 2010: 3080-3083 58 | + Dennis Bazow, Ulrich Heinz, Michael Strickland:Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA. Comput. Phys. Commun. 225: 92-113 (2018) 59 | + Witold Zorski, Piotr Sklodowski:Object tracking and recognition using massively parallel processing with CUDA. MMAR 2015: 977-982 60 | + Witold Zorski, Piotr Sklodowski:Unknown scale objects recognition using massively parallel processing with CUDA. MMAR 2014: 770-775 61 | + Yongchao Liu, Bertil Schmidt, Douglas L. Maskell:DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI. BMC Bioinform. 12: 85 (2011) 62 | 63 | 64 | ### Application 65 | + Konstantinos Nikitopoulos:Massively Parallel, Nonlinear Processing for 6G: Potential Gains and Further Research Challenges. IEEE Commun. Mag. 60(1): 81-87 (2022) 66 | + Mingyu Kim, Nakhoon Baek:A 3D graphics rendering pipeline implementation based on the openCL massively parallel processing. J. Supercomput. 77(7): 7351-7367 (2021) 67 | + Florian Gouin:Méthodologie de placement d'algorithmes de traitement d'images sur architecture massivement parallèle. (Mapping methodology for image processing applications on massively parallel architectures). Paris Sciences et Lettres University, France, 2019 68 | + Yu Huang, Long Zheng, Xiaofei Liao, Hai Jin, Pengcheng Yao, Chuangyi Gui:RAGra: Leveraging Monolithic 3D ReRAM for Massively-Parallel Graph Processing. DATE 2019: 1273-1276 69 | + Michael Kenzel, Bernhard Kerbl, Wolfgang Tatzgern, Elena Ivanchenko, Dieter Schmalstieg, Markus Steinberger:On-the-fly Vertex Reuse for Massively-Parallel Software Geometry Processing. CoRR abs/1805.08893 (2018)2017 70 | + Aurélien Plyer, Guy Le Besnerais, Frédéric Champagnat:Massively parallel Lucas Kanade optical flow for real-time video processing applications. J. Real Time Image Process. 11(4): 713-730 (2016) 71 | + Marek Vinkler, Jirí Bittner, Vlastimil Havran, Michal Hapala:Massively Parallel Hierarchical Scene Processing with Applications in Rendering. Comput. Graph. Forum 32(8): 13-25 (2013) 72 | + Abdessamad Klilou, Said Belkouch, Philippe Elleaume, Philippe Le Gall, François Bourzeix, Moha M'rabet Hassani:Real-time parallel implementation of Pulse-Doppler radar signal processing chain on a massively parallel machine based on multi-core DSP and Serial RapidIO interconnect. EURASIP J. Adv. Signal Process. 2014: 161 (2014) 73 | + Riccardo Zanella, Francesco Ceccon:TEMPeRA: TEmplate massively PaRAllel library for efficient n-dimensional signal processing. HPCS 2014: 643-650 74 | + Björn Lohrmann, Daniel Warneke, Odej Kao:Massively-parallel stream processing under QoS constraints with Nephele. HPDC 2012: 271-282 75 | + Takashi Kurafuji, Masaru Haraguchi, Masami Nakajima, Tetsu Nishijima, Tetsushi Tanizaki, Hiroyuki Yamasaki, Takeaki Sugimura, Yuta Imai, Masakatsu Ishizaki, Takeshi Kumaki, Kan Murata, Kanako Yoshida, Eisuke Shimomura, Hideyuki Noda, Yoshihiro Okuno, Shunsuke Kamijo, Tetsushi Koide, Hans Jürgen Mattausch, Kazutami Arimoto:A Scalable Massively Parallel Processor for Real-Time Image Processing. IEEE J. Solid State Circuits 46(10): 2363-2373 (2011) 76 | + Takashi Kurafuji, Masaru Haraguchi, Masami Nakajima, Takayuki Gyohten, Tetsu Nishijima, Hiroyuki Yamasaki, Yuta Imai, Masakatsu Ishizaki, Takeshi Kumaki, Yoshihiro Okuno, Tetsushi Koide, Hans Jürgen Mattausch, Kazutami Arimoto:A scalable massively parallel processor for real-time image processing. ISSCC 2010: 334-335 77 | 78 | 79 | ### System 80 | + Paraschos Koutris, Dan Suciu:A Guide to Formal Analysis of Join Processing in Massively Parallel Systems. SIGMOD Rec. 45(4): 18-27 (2016) 81 | + Michael Sherry, Andy Shearer:IMPAIR: massively parallel deconvolution on the GPU. Image Processing: Algorithms and Systems 2013: 86550Q 82 | + Karthikeyan Balasubramanian, Iyad Obeid:Massively parallel neural signal processing: System-on-Chip design with FPGAs. EMBC 2011: 4609-4612 2010 83 | + Ian A. Willson:The Evolution of the Massively Parallel Processing Database in Support of Visual Analytics. Inf. Resour. Manag. J. 24(4): 1-26 (2011) 84 | 85 | 86 | ### Query & Execution 87 | + Sai Wu, Feng Li, Sharad Mehrotra, Beng Chin Ooi:Query optimization for massively parallel data processing. SoCC 2011: 12 88 | + Steven Solomon, Parimala Thulasiraman, Ruppa K. Thulasiram:Scheduling Using Multiple Swarm Particle Optimization with Memetic Features on Graphics Processing Units. Massively Parallel Evolutionary Computation on GPGPUs 2013: 149-178 89 | 90 | 91 | ### Method 92 | + John C. Steuben, Graham Mustoe, Cameron J. Turner:Massively Parallel Discrete Element Method Simulations on Graphics Processing Units. J. Comput. Inf. Sci. Eng. 16(3) (2016) 93 | + Frédéric Magoulès, Abal-Kassim Cheik Ahamed:Alinea: An Advanced Linear Algebra Library for Massively Parallel Computations on Graphics Processing Units. Int. J. High Perform. Comput. Appl. 29(3): 284-310 (2015) 94 | + Alexandru Tanase, Michael Witterauf, Jürgen Teich, Frank Hannig, Vahid Lari:On-demand fault-tolerant loop processing on massively parallel processor arrays. ASAP 2015: 194-201 95 | + Nikolay Golov, Lars Rönnbäck:Big Data Normalization for Massively Parallel Processing Databases. ER Workshops 2015: 154-163 96 | + Moufida Rehab Adjout, Faouzi Boufarès:A Massively Parallel Processing for the Multiple Linear Regression. SITIS 2014: 666-671 97 | + Muzaffer Can Altinigneli, Claudia Plant, Christian Böhm:Massively parallel expectation maximization using graphics processing units. KDD 2013: 838-846 98 | + William B. Langdon:Large-Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units. Massively Parallel Evolutionary Computation on GPGPUs 2013: 311-347 99 | + Ze Deng, Dan Chen, Yangyang Hu, Xiaoming Wu, Weizhou Peng, Xiaoli Li:Massively parallel non-stationary EEG data processing on GPGPU platforms with Morlet continuous wavelet transform. J. Internet Serv. Appl. 3(3): 347-357 (2012) 100 | + Lorenzo Dematté:Smoldyn on Graphics Processing Units: Massively Parallel Brownian Dynamics Simulations. IEEE ACM Trans. Comput. Biol. Bioinform. 9(3): 655-667 (2012) 101 | + A. Stephen McGough, S. Liang, M. Rapoportas, R. Grey, G. Kumar Vinod, D. Maddy, A. Trueman, John Wainwright:Massively parallel landscape-evolution modelling using general purpose graphical processing units. HiPC 2012: 1-10 102 | + Hana Krichene, Mouna Baklouti, Mohamed Abid, Philippe Marquet, Jean-Luc Dekeyser:Broadcast with mask on a massively parallel processing on a chip. HPCS 2012: 275-280 103 | + Trung Dac Nguyen, Carolyn L. Phillips, Joshua A. Anderson, Sharon C. Glotzer:Rigid body constraints realized in massively-parallel molecular dynamics on graphics processing units. Comput. Phys. Commun. 182(11): 2307-2313 (2011) 104 | 105 | 106 | ### Other 107 | + Björn Lohrmann:Massively parallel stream processing with latency guarantees. Technical University of Berlin, Germany, 2016 108 | + Daniel Warneke:Massively Parallel Data Processing on Infrastructure as a Service Platforms. Berlin Institute of Technology, 2011 -------------------------------------------------------------------------------- /Paper/NVM数据库技术.md: -------------------------------------------------------------------------------- 1 | ## 综述： 2 | 3 | - 潘巍,李战怀,杜洪涛,周陈超,苏静.新型非易失存储环境下事务型数据管理技术研究.软件学报,2017,28(1) 4 | - Justin A. DeBrabant, Joy Arulraj, Andrew Pavlo, Michael Stonebraker, Stanley B. Zdonik, Subramanya Dulloor: A Prolegomenon on OLTP Database Systems for Non-Volatile Memory. ADMS@VLDB 2014: 57-63 5 | - Stratis D. Viglas: Data Management in Non-Volatile Memory. SIGMOD Conference 2015: 1707-1711 6 | - Shashank Gugnani, Arjun Kashyap, Xiaoyi Lu: Understanding the Idiosyncrasies of Real Persistent Memory. Proc. VLDB Endow. 14(4): 626-639 (2020) 7 | - Dimitrios Koutsoukos, Raghav Bhartia, Ana Klimovic, Gustavo Alonso:How to use Persistent Memory in your Database. CoRR abs/2112.00425 (2021) 8 | 9 | ## 系统架构\存储： 10 | 11 | - Justin A. DeBrabant, Andrew Pavlo, Stephen Tu, Michael Stonebraker, Stanley B. Zdonik: Anti-Caching: A New Approach to Database Management System Architecture. Proc. VLDB Endow. 6(14): 1942-1953 (2013) 12 | 13 | - Ruicheng Liu, Peiquan Jin, Xiaoliang Wang, Zhou Zhang, Shouhong Wan, Bei Hua: NVLevel: A High Performance Key-Value Store for Non-Volatile Memory. HPCC/SmartCity/DSS 2019: 1020-1027 14 | 15 | - Lawrence Benson, Hendrik Makait, Tilmann Rabl: Viper: An Efficient Hybrid PMem-DRAM Key-Value Store. Proc. VLDB Endow. 14(9): 1544-1556 (2021) 16 | 17 | - Gang Liu, Leying Chen, Shimin Chen: Zen: a High-Throughput Log-Free OLTP Engine for Non-Volatile Main Memory. Proc. VLDB Endow. 14(5): 835-848 (2021) 18 | 19 | - Hideaki Kimura: FOEDUS: OLTP Engine for a Thousand Cores and NVRAM. SIGMOD Conference 2015: 691-706 20 | 21 | - Alexander van Renen, Viktor Leis, Alfons Kemper, Thomas Neumann, Takushi Hashida, Kazuichi Oe, Yoshiyasu Doi, Lilian Harada, Mitsuru Sato: Managing Non-Volatile Memory in Database Systems. SIGMOD Conference 2018: 1541-1555 22 | 23 | - Xianzhang Chen, Edwin Hsing-Mean Sha, Ahmad Abdullah, Qingfeng Zhuge, Lin Wu, Chaoshu Yang, Weiwen Jiang: UDORN: A design framework of persistent in-memory key-value database for NVM. NVMSA 2017:1-6 24 | 25 | - Ahmad Hassan, Dimitrios S. Nikolopoulos, Hans Vandierendonck:Fast and Energy-Efficient OLAP Data Management on Hybrid Main Memory Systems. IEEE Trans. Computers 68(11): 1597-1611 (2019) 26 | 27 | - Jinfeng Yang, Bingzhe Li, David J. Lilja:HeuristicDB: a hybrid storage database system using a non-volatile memory block device. SYSTOR 2021: 16:1-16:12 28 | 29 | - Robert Lasch, Robert Schulze, Thomas Legler, Kai-Uwe Sattler: Workload-Driven Placement of Column-Store Data Structures on DRAM and NVM. DaMoN 2021: 5:1-5:8 30 | 31 | - Yongping Luo, Peiquan Jin, Shouhong Wan: Optimal Data Placement for Data-Centric Algorithms on NVM-Based Hybrid Memory. DSAA 2020: 226-235 32 | 33 | 34 | 35 | ## 恢复技术研究： 36 | 37 | - Steven Pelley, Thomas F. Wenisch, Brian T. Gold, Bill Bridge: Storage Management in the NVRAM Era. Proc. VLDB Endow. 7(2): 121-132 (2013) 38 | 39 | - Joel Coburn, Trevor Bunker, Meir Schwarz, Rajesh Gupta, Steven Swanson: From ARIES to MARS: transaction support for next-generation, solid-state drives. SOSP 2013: 197-212 40 | 41 | - Jian Huang, Karsten Schwan, Moinuddin K. Qureshi: NVRAM-aware Logging in Transaction Systems. Proc. VLDB Endow. 8(4): 389-400 (2014) 42 | 43 | - Shen Gao, Jianliang Xu, Theo Härder, Bingsheng He, Byron Choi, Haibo Hu: PCMLogging: Optimizing Transaction Logging and Recovery Performance with PCM. IEEE Trans. Knowl. Data Eng. 27(12): 3332-3346 (2015) 44 | 45 | - Joy Arulraj, Matthew Perron, Andrew Pavlo: Write-Behind Logging. Proc. VLDB Endow. 10(4): 337-348 (2016) 46 | 47 | - Yongseok Son, Hara Kang, Heon Young Yeom, Hyuck Han: 2017. A log-structured buffer for database systems using non-volatile memory. In Proceedings of the Symposium on Applied Computing (SAC '17). Association for Computing Machinery, New York, NY, USA, 880–886. 48 | 49 | - Ru Fang, Hui-I Hsiao, Bin He, C. Mohan, Yun Wang: High performance database logging using storage class memory. ICDE 2011: 1221-1231 50 | 51 | - Jian Huang, Karsten Schwan, Moinuddin K. Qureshi: NVRAM-aware Logging in Transaction Systems. Proc. VLDB Endow. 8(4): 389-400 (2014) 52 | 53 | - Joy Arulraj, Andrew Pavlo, Subramanya Dulloor: Let's Talk About Storage & Recovery Methods for Non-Volatile Memory Database Systems. SIGMOD Conference 2015: 707-722 54 | 55 | - Tianzheng Wang, Ryan Johnson: Scalable Logging through Emerging Non-Volatile Memory. Proc. VLDB Endow. 7(10): 865-876 (2014) 56 | 57 | - Ismail Oukid, Daniel Booss, Wolfgang Lehner, Peter Bumbulis, Thomas Willhalm: SOFORT: a hybrid SCM-DRAM storage engine for fast data recovery. DaMoN 2014: 8:1-8:7 58 | 59 | - Aasheesh Kolli, Steven Pelley, Ali G. Saidi, Peter M. Chen, Thomas F. Wenisch: High-Performance Transactions for Persistent Memories. ASPLOS 2016: 399-411 60 | 61 | 62 | 63 | ## 并发控制技术研究： 64 | 65 | - Per-Åke Larson, Spyros Blanas, Cristian Diaconu, Craig Freedman, Jignesh M. Patel, Mike Zwilling: High-Performance Concurrency Control Mechanisms for Main-Memory Databases. Proc. VLDB Endow. 5(4): 298-309 (2011) 66 | - Ryan Johnson, Ippokratis Pandis, Anastasia Ailamaki: Improving OLTP Scalability using Speculative Lock Inheritance. Proc. VLDB Endow. 2(1): 479-489 (2009) 67 | - Ippokratis Pandis, Ryan Johnson, Nikos Hardavellas, Anastasia Ailamaki: 68 | Data-Oriented Transaction Execution. Proc. VLDB Endow. 3(1): 928-939 (2010) 69 | - Ippokratis Pandis, Pinar Tözün, Miguel Branco, Dimitris Karampinas, Danica Porobic, Ryan Johnson, Anastasia Ailamaki: A data-oriented transaction execution engine and supporting tools. SIGMOD Conference 2011: 1237-1240 70 | - Kun Ren, Alexander Thomson, Daniel J. Abadi: Lightweight Locking for Main Memory Database Systems. Proc. VLDB Endow. 6(2): 145-156 (2012) 71 | - Kun Ren, Alexander Thomson, Daniel J. Abadi: VLL: a lock manager redesign for main memory database systems. VLDB J. 24(5): 681-705 (2015) 72 | 73 | 74 | 75 | ## 索引技术研究： 76 | 77 | - Shimin Chen, Qin Jin: Persistent B+-Trees in Non-Volatile Main Memory. Proc. VLDB Endow. 8(7): 786-797 (2015) 78 | - Pengfei Zuo, Yu Hua, Jie Wu: Write-Optimized and High-Performance Hashing Index Scheme for Persistent Memory. OSDI 2018: 461-476 79 | - Se Kwon Lee, Jayashree Mohan, Sanidhya Kashyap, Taesoo Kim, Vijay Chidambaram: Recipe: converting concurrent DRAM indexes to persistent-memory indexes. SOSP 2019: 462-477 80 | - Moohyeon Nam, Hokeun Cha, Young-ri Choi, Sam H. Noh, Beomseok Nam: Write-Optimized Dynamic Hashing for Persistent Memory. FAST 2019: 31-44 81 | - Yoav Zuriel, Michal Friedman, Gali Sheffi, Nachshon Cohen, Erez Petrank: 82 | Efficient lock-free durable sets. Proc. ACM Program. Lang. 3(OOPSLA): 128:1-128:26 (2019) 83 | - Baotong Lu, Xiangpeng Hao, Tianzheng Wang, Eric Lo: Dash: Scalable Hashing on Persistent Memory. Proc. VLDB Endow. 13(8): 1147-1161 (2020) 84 | - Zhangyu Chen, Yu Hua, Bo Ding, Pengfei Zuo: Lock-free Concurrent Level Hashing for Persistent Memory. USENIX Annual Technical Conference 2020: 799-812 85 | - Daokun Hu, Zhiwen Chen, Jianbing Wu, Jianhua Sun, Hao Chen: Persistent Memory Hash Indexes: An Experimental Evaluation. Proc. VLDB Endow. 14(5): 785-798 (2021) 86 | - Saeed Kargar, Faisal Nawab: Hamming Tree: The Case for Memory-Aware Bit Flipping Reduction for NVM Indexing. CIDR 2021 87 | 88 | 89 | 90 | ## 排序技术研究： 91 | 92 | - Zhaole Chu, Yongping Luo, Peiquan Jin: An Efficient Sorting Algorithm for Non-Volatile Memory. 93 | International Journal of Software Engineering and Knowledge Engineering (2021): n. pag. 94 | 95 | - Yongping Luo, Zhaole Chu, Peiquan Jin, Shouhong Wan: Efficient Sorting and Join on NVM-Based Hybrid Memory. ICA3PP (1) 2020: 15-30 96 | 97 | - Mohammed Bey Ahmed Khernache, Arezki Laga, Jalil Boukhobza: MONTRES-NVM: An External Sorting Algorithm for Hybrid Memory. NVMSA 2018: 49-54 98 | 99 | -------------------------------------------------------------------------------- /Paper/NewSQL数据库技术.md: -------------------------------------------------------------------------------- 1 | ## NewSQL Database 2 | 3 | ### Survey 4 | + Jason Arnold, Boris Glavic, Ioan Raicu:HRDBMS: A NewSQL Database for Analytics. CLUSTER 2015: 519-520 5 | + Andrew Pavlo, Matthew Aslett:What's Really New with NewSQL? SIGMOD Rec. 45(2): 45-55 (2016) 6 | + Kian Win Ong, Yannis Papakonstantinou, Romain Vernoux:The SQL++ Semi-structured Data Model and Query Language: A Capabilities Survey of SQL-on-Hadoop, NoSQL and NewSQL Databases. CoRR abs/1405.3631 (2014) 7 | 8 | ### System 9 | + Patrick Valduriez, Ricardo Jiménez-Peris, M. Tamer Özsu:Distributed Database Systems: The Case for NewSQL. Trans. Large Scale Data Knowl. Centered Syst. 48: 1-15 (2021) 10 | + Salman Niazi, Mahmoud Ismail, Gautier Berthou, Jim Dowling:Leader Election Using NewSQL Database Systems. DAIS 2015: 158-172 11 | + Li-Yan Yuan, Lengdong Wu, Jia-Huai You, Yan Chi:A Demonstration of Rubato DB: A Highly Scalable NewSQL Database System for OLTP and Big Data Applications. SIGMOD Conference 2015: 907-912 12 | + João Oliveira, Jorge Bernardino:NewSQL Databases - MemSQL and VoltDB Experimental Evaluation. KEOD 2017: 276-281 2016 13 | 14 | ### Architecture 15 | + Natalia Chaudhry, Muhammad Murtaza Yousaf:Architectural assessment of NoSQL and NewSQL systems. Distributed Parallel Databases 38(4): 881-926 (2020) 16 | + Kshitij A. Doshi, Tao Zhong, Zhongyan Lu, Xi Tang, Ting Lou, Gang Deng:Blending SQL and NewSQL Approaches: Reference Architectures for Enterprise Big Data Challenges. CyberC 2013: 163-170 17 | 18 | 19 | ### Performance 20 | + María Murazzo, Pablo Gómez, Nelson Rodríguez, Diego Medel:Database NewSQL Performance Evaluation for Big Data in the Public Cloud. JCC&BD 2019: 110-121 21 | + Arjun Pandya, Chaitanya Kulkarni, Kunal Mali, Jianwu Wang:An Open Source Cloud-Based NoSQL and NewSQL Database Benchmarking Platform for IoT Data. Bench 2018: 65-77 22 | 23 | 24 | ### Operator 25 | 26 | + Pethuru Raj:Chapter One - A Detailed Analysis of NoSQL and NewSQL Databases for Bigdata Analytics and Distributed Computing. Adv. Comput. 109: 1-48 (2018) 27 | + Siddhartha Duggirala:Chapter Two - NewSQL Databases and Scalable In-Memory Analytics. Adv. Comput. 109: 49-76 (2018) 28 | 29 | 30 | ### Application 31 | + Geomar Andre Schreiner, Denio Duarte, Guilherme Dal Bianco, Ronaldo dos Santos Mello:A Hybrid Partitioning Strategy for NewSQL Databases: The VoltDB Case. iiWAS 2019: 353-360 32 | + Geomar Andre Schreiner, Denio Duarte, Ronaldo dos Santos Mello:An Autonomous Hybrid Data Partition for NewSQL DBs. SBBD Companion 2018: 95-101 33 | + Geomar Andre Schreiner, Ronan Knob, Denio Duarte, Patricia Vilain, Ronaldo dos Santos Mello:NewSQL Through the Looking Glass. iiWAS 2019: 361-369 34 | + Petri Kotiranta, Marko Junkkari:Effectiveness of NoSQL and NewSQL Databases in Mobile Network Event Data: Cassandra and ParStream/Kinetic. Open J. Databases 5(1): 1-13 (2018) 35 | + Dylan Hutchison, Jeremy Kepner, Vijay Gadepally, Bill Howe:From NoSQL Accumulo to NewSQL Graphulo: Design and utility of graph algorithms inside a BigTable database. HPEC 2016: 1-9 36 | + Dylan Hutchison, Jeremy Kepner, Vijay Gadepally, Bill Howe:From NoSQL Accumulo to NewSQL Graphulo: Design and Utility of Graph Algorithms inside a BigTable Database. CoRR abs/1606.07085 (2016) 37 | + Ugur Çetintemel, Jiang Du, Tim Kraska, Samuel Madden, David Maier, John Meehan, Andrew Pavlo, Michael Stonebraker, Erik Sutherland, Nesime Tatbul, Kristin Tufte, Hao Wang, Stanley B. Zdonik:S-Store: A Streaming NewSQL System for Big Velocity Applications. Proc. VLDB Endow. 7(13): 1633-1636 (2014) 38 | 39 | 40 | ### File System 41 | + Salman Niazi, Mahmoud Ismail, Seif Haridi, Jim Dowling:HopsFS: Scaling Hierarchical File System Metadata Using NewSQL Databases. Encyclopedia of Big Data Technologies 2019 42 | + Salman Niazi:Scaling Distributed Hierarchical File Systems Using NewSQL Databases. Royal Institute of Technology, Stockholm, Sweden, 2018 43 | + Salman Niazi, Mahmoud Ismail, Seif Haridi, Jim Dowling, Steffen Grohsschmiedt, Mikael Ronström:HopsFS: Scaling Hierarchical File System Metadata Using NewSQL Databases. FAST 2017: 89-104 44 | + Salman Niazi, Mahmoud Ismail, Steffen Grohsschmiedt, Mikael Ronström, Seif Haridi, Jim Dowling:HopsFS: Scaling Hierarchical File System Metadata Using NewSQL Databases. CoRR abs/1606.01588 (2016) 45 | 46 | 47 | ### Data Manager 48 | 49 | + Zoran Majkic:Intensional RDB Manifesto: a Unifying NewSQL Model for Flexible Big Data. CoRR abs/1403.0017 (2014) 50 | + Katarina Grolinger, Wilson A. Higashino, Abhinav Tiwari, Miriam A. M. Capretz:Data management in cloud environments: NoSQL and NewSQL data stores. J. Cloud Comput. 2: 22 (2013) 51 | + A. B. M. Moniruzzaman:NewSQL: Towards Next-Generation Scalable RDBMS for Online Transaction Processing (OLTP) for Big Data Management. CoRR abs/1411.7343 (2014) 52 | 53 | ### other 54 | 55 | + Jeremy Kepner, Vijay Gadepally, Dylan Hutchison, Hayden Jananthan, Timothy G. Mattson, Siddharth Samsi, Albert Reuther:Associative array model of SQL, NoSQL, and NewSQL databases. HPEC 2016: 1-9 56 | + Guthemberg Silvestre, Carla Sauvanaud, Mohamed Kaâniche, Karama Kanoun:Tejo: A Supervised Anomaly Detection Scheme for NewSQL Databases. SERENE 2015: 114-127 57 | 58 | -------------------------------------------------------------------------------- /Paper/RDMA相关数据库优化技术.md: -------------------------------------------------------------------------------- 1 | # RDMA 2 | 3 | 1. [Magdalena Pröbstl](https://dblp.org/pid/300/9688.html), [Philipp Fent](https://dblp.org/pid/265/9606.html), [Maximilian E. Schüle](https://dblp.org/pid/204/3776.html), [Moritz Sichert](https://dblp.org/pid/300/9685.html), [Thomas Neumann](https://dblp.org/pid/n/ThomasNeumann.html), [Alfons Kemper](https://dblp.org/pid/k/AlfonsKemper.html): 4 | **One Buffer Manager to Rule Them All: Using Distributed Memory with Cache Coherence over RDMA.** [ADMS@VLDB 2021](https://dblp.org/db/conf/adms/adms2021.html#ProbstlFSS0K21): 17-26 5 | 6 | 2. [Chenchen Huang](https://dblp.org/pid/119/0896.html), [Huiqi Hu](https://dblp.org/pid/137/1395.html), [Xuecheng Qi](https://dblp.org/pid/219/9480.html), [Xuan Zhou](https://dblp.org/pid/32/3943-1.html), [Aoying Zhou](https://dblp.org/pid/z/AoyingZhou.html): 7 | **RS-store: RDMA-enabled skiplist-based key-value store for efficient range query.** [Frontiers Comput. Sci. 15(6)](https://dblp.org/db/journals/fcsc/fcsc15.html#HuangHQZZ21): 156617 (2021) 8 | 9 | 3. [Ziyu Zhang](https://dblp.org/pid/90/46.html)![0000-0001-6293-7227](https://dblp.org/img/orcid-mark.12x12.png), [Zitan Liu](https://dblp.org/pid/295/6604.html), [Qingcai Jiang](https://dblp.org/pid/262/1511.html), [Junshi Chen](https://dblp.org/pid/120/1745.html), [Hong An](https://dblp.org/pid/73/5552.html): 10 | **RDMA-Based Apache Storm for High-Performance Stream Data Processing.** [Int. J. Parallel Program. 49(5)](https://dblp.org/db/journals/ijpp/ijpp49.html#ZhangLJCA21): 671-684 (2021) 11 | 12 | 4. [Qingfeng Zhuge](https://dblp.org/pid/73/1987.html), [Hao Zhang](https://dblp.org/pid/55/2270.html), [Edwin Hsing-Mean Sha](https://dblp.org/pid/27/2376.html)![0000-0001-5605-5631](https://dblp.org/img/orcid-mark.12x12.png), [Rui Xu](https://dblp.org/pid/00/4859.html), [Jun Liu](https://dblp.org/pid/95/3736.html), [Shengyu Zhang](https://dblp.org/pid/47/3459.html): 13 | **Exploring Efficient Architectures on Remote In-Memory NVM over RDMA.** [ACM Trans. Embed. Comput. Syst. 20(5s)](https://dblp.org/db/journals/tecs/tecs20.html#ZhugeZSXLZ21): 73:1-73:20 (2021) 14 | 15 | 5. [Xingda Wei](https://dblp.org/pid/168/9037.html), [Rong Chen](https://dblp.org/pid/22/6904-1.html), [Haibo Chen](https://dblp.org/pid/31/6601-1.html), [Binyu Zang](https://dblp.org/pid/86/680.html): 16 | **XStore: Fast RDMA-Based Ordered Key-Value Store Using Remote Learned Cache.** [ACM Trans. Storage 17(3)](https://dblp.org/db/journals/tos/tos17.html#WeiCCZ21): 18:1-18:32 (2021) 17 | 18 | 6. [Bohong Zhu](https://dblp.org/pid/247/9449.html), [Youmin Chen](https://dblp.org/pid/203/1564.html), [Qing Wang](https://dblp.org/pid/97/6505.html), [Youyou Lu](https://dblp.org/pid/96/8005.html), [Jiwu Shu](https://dblp.org/pid/60/3690.html): 19 | **Octopus+: An RDMA-Enabled Distributed Persistent Memory File System.** [ACM Trans. Storage 17(3)](https://dblp.org/db/journals/tos/tos17.html#ZhuCWLS21): 19:1-19:25 (2021) 20 | 21 | 7. [Magdalena Pröbstl](https://dblp.org/pid/300/9688.html), [Philipp Fent](https://dblp.org/pid/265/9606.html), [Maximilian E. Schüle](https://dblp.org/pid/204/3776.html), [Moritz Sichert](https://dblp.org/pid/300/9685.html), [Thomas Neumann](https://dblp.org/pid/n/ThomasNeumann.html), [Alfons Kemper](https://dblp.org/pid/k/AlfonsKemper.html): 22 | **One Buffer Manager to Rule Them All: Using Distributed Memory with Cache Coherence over RDMA.** [ADMS@VLDB 2021](https://dblp.org/db/conf/adms/adms2021.html#ProbstlFSS0K21): 17-26 23 | 24 | 8. [Alexander Kumaigorodski](https://dblp.org/pid/287/7559.html), [Clemens Lutz](https://dblp.org/pid/165/8314.html)![0000-0002-6193-4734](https://dblp.org/img/orcid-mark.12x12.png), [Volker Markl](https://dblp.org/pid/m/VMarkl.html): 25 | **Fast CSV Loading Using GPUs and RDMA for In-Memory Data Processing.** [BTW 2021](https://dblp.org/db/conf/btw/btw2021.html#KumaigorodskiLM21): 19-38 26 | 27 | 9. [Juhyun Bae](https://dblp.org/pid/247/5910.html), [Ling Liu](https://dblp.org/pid/l/LingLiu.html), [Yanzhao Wu](https://dblp.org/pid/61/9620.html), [Gong Su](https://dblp.org/pid/18/6259.html), [Arun Iyengar](https://dblp.org/pid/i/ArunIyengar.html): 28 | **RDMAbox: Optimizing RDMA for Memory Intensive Workload.** [CIC 2021](https://dblp.org/db/conf/coinco/cic2021.html#BaeLWSI21): 1-10 29 | 30 | 10. [Xinxin Liu](https://dblp.org/pid/99/3477.html), [Yu Hua](https://dblp.org/pid/52/3115-1.html), [Rong Bai](https://dblp.org/pid/233/5736.html): 31 | **Consistent RDMA-Friendly Hashing on Remote Persistent Memory.** [ICCD 2021](https://dblp.org/db/conf/iccd/iccd2021.html#Liu0B21): 174-177 32 | 33 | 11. [Xinxin Liu](https://dblp.org/pid/99/3477.html), [Yu Hua](https://dblp.org/pid/52/3115-1.html), [Xuan Li](https://dblp.org/pid/64/5016.html), [Qifan Liu](https://dblp.org/pid/243/3163.html): 34 | **Write-Optimized and Consistent RDMA-based Non-Volatile Main Memory Systems.** [ICCD 2021](https://dblp.org/db/conf/iccd/iccd2021.html#Liu0LL21): 248-251 35 | 36 | 12. [Sijie Wu](https://dblp.org/pid/41/10775.html), [Hanhua Chen](https://dblp.org/pid/54/2930.html), [Yonghui Wang](https://dblp.org/pid/79/3041.html), [Hai Jin](https://dblp.org/pid/98/4156.html): 37 | **Argus: Efficient Job Scheduling in RDMA-assisted Big Data Processing.** [IPDPS 2021](https://dblp.org/db/conf/ipps/ipdps2021.html#WuCW021): 827-836 38 | 39 | 13. [Ines Messadi](https://dblp.org/pid/223/7104.html), [Shivananda Neumann](https://dblp.org/pid/253/7203.html), [Nico Weichbrodt](https://dblp.org/pid/185/6233.html), [Lennart Almstedt](https://dblp.org/pid/253/7142.html), [Mohammad Mahhouk](https://dblp.org/pid/297/5552.html), [Rüdiger Kapitza](https://dblp.org/pid/27/2697.html): 40 | **Precursor: a fast, client-centric and trusted key-value store using RDMA and Intel SGX.** [Middleware 2021](https://dblp.org/db/conf/middleware/middleware2021.html#MessadiNWAMK21): 1-13 41 | 42 | 14. [Xing Wei](https://dblp.org/pid/14/4301.html), [Huiqi Hu](https://dblp.org/pid/137/1395.html), [Xuan Zhou](https://dblp.org/pid/32/3943-1.html), [Aoying Zhou](https://dblp.org/pid/z/AoyingZhou.html): 43 | **LinKV: An RDMA-Enabled KVS for High Performance and Strict Consistency Under Skew.** [APWeb/WAIM (1) 2021](https://dblp.org/db/conf/apweb/apweb2021-1.html#WeiHZZ21): 190-198 44 | 45 | 15. [Tianxi Li](https://dblp.org/pid/133/6835.html), [Haiyang Shi](https://dblp.org/pid/207/3468.html), [Xiaoyi Lu](https://dblp.org/pid/27/5660.html): 46 | **HatRPC: hint-accelerated thrift RPC over RDMA.** [SC 2021](https://dblp.org/db/conf/sc/sc2021.html#LiSL21): 36:1-36:14 47 | 48 | 16. [Jie Tan](https://dblp.org/pid/81/7419.html), [Hanhua Chen](https://dblp.org/pid/54/2930.html), [Yonghui Wang](https://dblp.org/pid/79/3041.html), [Hai Jin](https://dblp.org/pid/98/4156.html): 49 | **Whale: efficient one-to-many data partitioning in RDMA-assisted distributed stream processing systems.** [SC 2021](https://dblp.org/db/conf/sc/sc2021.html#TanCW021): 101:1-101:12 50 | 51 | 17. [Konstantin Taranov](https://dblp.org/pid/206/7118.html), [Salvatore Di Girolamo](https://dblp.org/pid/165/7302.html), [Torsten Hoefler](https://dblp.org/pid/16/3869.html): 52 | **CoRM: Compactable Remote Memory over RDMA.** [SIGMOD Conference 2021](https://dblp.org/db/conf/sigmod/sigmod2021.html#TaranovGH21): 1811-1824 53 | 54 | 18. [Erfan Zamanian](https://dblp.org/pid/141/9169.html): 55 | **Scalable Distributed Transaction Processing on Modern RDMA-enabled Networks.** [SIGMOD Conference 2021](https://dblp.org/db/conf/sigmod/sigmod2021.html#Zamanian21): 8 56 | 57 | 19. [Xingda Wei](https://dblp.org/pid/168/9037.html), [Xiating Xie](https://dblp.org/pid/244/9240.html), [Rong Chen](https://dblp.org/pid/22/6904-1.html), [Haibo Chen](https://dblp.org/pid/31/6601-1.html), [Binyu Zang](https://dblp.org/pid/86/680.html): 58 | **Characterizing and Optimizing Remote Persistent Memory with RDMA and NVM.** [USENIX Annual Technical Conference 2021](https://dblp.org/db/conf/usenix/usenix2021.html#WeiX00Z21): 523-536 59 | 60 | 20. [Yaofeng Tu](https://dblp.org/pid/248/2683.html), [Yinjun Han](https://dblp.org/pid/286/7590.html), [Hao Jin](https://dblp.org/pid/06/1958.html), [Zhenghua Chen](https://dblp.org/pid/03/7457.html), [Yanchao Zhao](https://dblp.org/pid/44/7814.html): 61 | **RDMA Based Performance Optimization on Distributed Database Systems: A Case Study with GoldenX.** [WASA (2) 2021](https://dblp.org/db/conf/wasa/wasa2021-2.html#TuHJCZ21): 237-248 62 | 63 | 21. [Xinxin Liu](https://dblp.org/pid/99/3477.html), [Yu Hua](https://dblp.org/pid/52/3115-1.html), [Rong Bai](https://dblp.org/pid/233/5736.html): 64 | **Consistent RDMA-Friendly Hashing on Remote Persistent Memory.** [CoRR abs/2107.06836](https://dblp.org/db/journals/corr/corr2107.html#abs-2107-06836) (2021) 65 | 66 | 22. [Michalis Vardoulakis](https://dblp.org/pid/304/4517.html), [Giorgos Saloustros](https://dblp.org/pid/165/8208.html), [Pilar González-Férez](https://dblp.org/pid/29/6255.html), [Angelos Bilas](https://dblp.org/pid/96/4578.html): 67 | **Using RDMA for Efficient Index Replication in LSM Key-Value Stores.** [CoRR abs/2110.09918](https://dblp.org/db/journals/corr/corr2110.html#abs-2110-09918) (2021) 68 | 69 | 23. [Jiwu Shu](https://dblp.org/pid/60/3690.html), [Youmin Chen](https://dblp.org/pid/203/1564.html), [Qing Wang](https://dblp.org/pid/97/6505.html), [Bohong Zhu](https://dblp.org/pid/247/9449.html), [Junru Li](https://dblp.org/pid/202/6826.html), [Youyou Lu](https://dblp.org/pid/96/8005.html): 70 | **TH-DPMS: Design and Implementation of an RDMA-enabled Distributed Persistent Memory Storage System.** [ACM Trans. Storage 16(4)](https://dblp.org/db/journals/tos/tos16.html#ShuCWZLL20): 24:1-24:31 (2020) 71 | 72 | 24. [Tinggang Wang](https://dblp.org/pid/250/1360.html), [Shuo Yang](https://dblp.org/pid/78/1102.html), [Hideaki Kimura](https://dblp.org/pid/98/5721-1.html), [Garret Swart](https://dblp.org/pid/46/4288.html), [Spyros Blanas](https://dblp.org/pid/65/5338.html): 73 | **Efficient Usage of One-Sided RDMA for Linear Probing.** [ADMS@VLDB 2020](https://dblp.org/db/conf/adms/adms2020.html#WangYKSB20): 1-13 74 | 75 | 25. [Xiao Zhang](https://dblp.org/pid/49/4478.html), [Binbin Liu](https://dblp.org/pid/22/7702.html), [Zian Gou](https://dblp.org/pid/291/6158.html), [Jia Shi](https://dblp.org/pid/43/10099.html), [Xiaonan Zhao](https://dblp.org/pid/15/3558.html): 76 | **DCache: A Distributed Cache Mechanism for HDFS based on RDMA.** [HPCC/DSS/SmartCity 2020](https://dblp.org/db/conf/hpcc/hpcc2020.html#ZhangLGSZ20): 283-291 77 | 78 | 26. [Jacob Nelson](https://dblp.org/pid/35/9256.html), [Roberto Palmieri](https://dblp.org/pid/09/7950.html): 79 | **On the Performance Impact of NUMA on One-sided RDMA Interactions.** [ICDCS 2020](https://dblp.org/db/conf/icdcs/icdcs2020.html#NelsonP20): 1181-1182 80 | 81 | 27. [Philipp Fent](https://dblp.org/pid/265/9606.html), [Alexander van Renen](https://dblp.org/pid/219/9679.html), [Andreas Kipf](https://dblp.org/pid/167/6469.html), [Viktor Leis](https://dblp.org/pid/10/11150.html), [Thomas Neumann](https://dblp.org/pid/n/ThomasNeumann.html), [Alfons Kemper](https://dblp.org/pid/k/AlfonsKemper.html): 82 | **Low-Latency Communication for Fast DBMS Using RDMA and Shared Memory.** [ICDE 2020](https://dblp.org/db/conf/icde/icde2020.html#FentRKL0K20): 1477-1488 83 | 84 | 28. [Xingda Wei](https://dblp.org/pid/168/9037.html), [Rong Chen](https://dblp.org/pid/22/6904-1.html), [Haibo Chen](https://dblp.org/pid/31/6601-1.html): 85 | **Fast RDMA-based Ordered Key-Value Store using Remote Learned Cache.** [OSDI 2020](https://dblp.org/db/conf/osdi/osdi2020.html#WeiCC20): 117-135 86 | 87 | 29. [Chao Wang](https://dblp.org/pid/188/7759-51.html), [Kezhao Huang](https://dblp.org/pid/264/1773.html), [Xuehai Qian](https://dblp.org/pid/42/5189.html): 88 | **A Comprehensive Evaluation of RDMA-enabled Concurrency Control Protocols.** [CoRR abs/2002.12664](https://dblp.org/db/journals/corr/corr2002.html#abs-2002-12664) (2020) 89 | 90 | 30. [Renke Wu](https://dblp.org/pid/192/7340.html), [Linpeng Huang](https://dblp.org/pid/93/4039.html), [Haojie Zhou](https://dblp.org/pid/38/1021.html): 91 | **RHKV: An RDMA and HTM friendly key-value store for data-intensive computing.** [Future Gener. Comput. Syst. 92](https://dblp.org/db/journals/fgcs/fgcs92.html#WuHZ19): 162-177 (2019) 92 | 93 | 31. [Bobo Huang](https://dblp.org/pid/246/5079.html)![0000-0002-9215-4298](https://dblp.org/img/orcid-mark.12x12.png), [Li Jin](https://dblp.org/pid/42/1899.html), [Zhihui Lu](https://dblp.org/pid/51/4748.html)![0000-0001-5706-7503](https://dblp.org/img/orcid-mark.12x12.png), [Ming Yan](https://dblp.org/pid/51/5332.html), [Jie Wu](https://dblp.org/pid/w/JieWu3.html), [Patrick C. K. Hung](https://dblp.org/pid/h/PCKHung.html), [Qifeng Tang](https://dblp.org/pid/144/9066.html): 94 | **RDMA-driven MongoDB: An approach of RDMA enhanced NoSQL paradigm for large-Scale data processing.** [Inf. Sci. 502](https://dblp.org/db/journals/isci/isci502.html#HuangJLYWHT19): 376-393 (2019) 95 | 96 | 32. [Wei Chen](https://dblp.org/pid/c/WeiChen9.html)![0000-0002-2876-6687](https://dblp.org/img/orcid-mark.12x12.png), [Songping Yu](https://dblp.org/pid/176/1206.html), [Zhiying Wang](https://dblp.org/pid/w/ZhiyingWang-3.html): 97 | **Fast In-Memory Key-Value Cache System with RDMA.** [J. Circuits Syst. Comput. 28(5)](https://dblp.org/db/journals/jcsc/jcsc28.html#ChenYW19): 1950074:1-1950074:30 (2019) 98 | 99 | 33. [Feilong Liu](https://dblp.org/pid/72/2526.html), [Lingyan Yin](https://dblp.org/pid/147/1330.html), [Spyros Blanas](https://dblp.org/pid/65/5338.html): 100 | **Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems.** [ACM Trans. Database Syst. 44(4)](https://dblp.org/db/journals/tods/tods44.html#LiuYB19): 17:1-17:45 (2019) 101 | 102 | 34. [Dipti Shankar](https://dblp.org/pid/153/6812.html), [Xiaoyi Lu](https://dblp.org/pid/27/5660.html), [Dhabaleswar K. Panda](https://dblp.org/pid/p/DhabaleswarKPanda.html): 103 | **SCOR-KV: SIMD-Aware Client-Centric and Optimistic RDMA-Based Key-Value Store for Emerging CPU Architectures.** [HiPC 2019](https://dblp.org/db/conf/hipc/hipc2019.html#ShankarLP19): 257-266 104 | 105 | 35. [Yi Wang](https://dblp.org/pid/17/221.html), [Yanan Jiang](https://dblp.org/pid/47/9957.html), [Qiufang Ma](https://dblp.org/pid/229/8840.html), [Chen Tian](https://dblp.org/pid/94/1247-1.html), [Bo Bai](https://dblp.org/pid/10/4008-1.html), [Gong Zhang](https://dblp.org/pid/77/6324.html): 106 | **RDMA Load Balancing via Data Partition.** [ICCCN 2019](https://dblp.org/db/conf/icccn/icccn2019.html#WangJMT0Z19): 1-8 107 | 108 | 36. [Mengbai Xiao](https://dblp.org/pid/160/4835.html), [Hao Wang](https://dblp.org/pid/w/HaoWang-2.html)![0000-0003-3557-6301](https://dblp.org/img/orcid-mark.12x12.png), [Liang Geng](https://dblp.org/pid/33/6908.html), [Rubao Lee](https://dblp.org/pid/41/6729.html), [Xiaodong Zhang](https://dblp.org/pid/37/4356-1.html): 109 | **Catfish: Adaptive RDMA-enabled R-Tree for Low Latency and High Throughput.** [ICDCS 2019](https://dblp.org/db/conf/icdcs/icdcs2019.html#XiaoWGL019): 164-175 110 | 111 | 37. [Runhua Zhang](https://dblp.org/pid/249/7886.html), [Yang Cheng](https://dblp.org/pid/27/986.html), [Jinkun Geng](https://dblp.org/pid/171/1013.html), [Shuai Wang](https://dblp.org/pid/42/1503-28.html), [Kaihui Gao](https://dblp.org/pid/249/7745.html), [Guowei Shen](https://dblp.org/pid/68/8604.html): 112 | **HiPower: A High-Performance RDMA Acceleration Solution for Distributed Transaction Processing.** [NPC 2019](https://dblp.org/db/conf/npc/npc2019.html#ZhangCGWGS19): 209-221 113 | 114 | 38. [Chengxin Guo](https://dblp.org/pid/234/5308.html), [Hong Chen](https://dblp.org/pid/52/4150-1.html), [Feng Zhang](https://dblp.org/pid/48/1294.html), [Cuiping Li](https://dblp.org/pid/03/6827-1.html): 115 | **Distributed Join Algorithms on Multi-CPU Clusters with GPUDirect RDMA.** [ICPP 2019](https://dblp.org/db/conf/icpp/icpp2019.html#GuoCZL19): 65:1-65:10 116 | 117 | 39. [Zhaogeng Li](https://dblp.org/pid/12/10813.html), [Ning Liu](https://dblp.org/pid/83/622.html), [Jiaoren Wu](https://dblp.org/pid/231/6109.html): 118 | **Toward a Production-ready General-purpose RDMA-enabled RPC.** [SIGCOMM Posters and Demos 2019](https://dblp.org/db/conf/sigcomm/p2019.html#LiLW19): 27-29 119 | 120 | 40. [ 121 | Tobias Ziegler](https://dblp.org/pid/146/6632.html), [Sumukha Tumkur Vani](https://dblp.org/pid/243/2403.html), [Carsten Binnig](https://dblp.org/pid/45/1559.html), [Rodrigo Fonseca](https://dblp.org/pid/f/RFonseca.html), [Tim Kraska](https://dblp.org/pid/26/6037.html): 122 | **Designing Distributed Tree-based Index Structures for Fast RDMA-capable Networks.** [SIGMOD Conference 2019](https://dblp.org/db/conf/sigmod/sigmod2019.html#0001VBFK19): 741-758 123 | 124 | 41. [Xingda Wei](https://dblp.org/pid/168/9037.html), [Zhiyuan Dong](https://dblp.org/pid/43/8717.html), [Rong Chen](https://dblp.org/pid/22/6904-1.html), [Haibo Chen](https://dblp.org/pid/31/6601-1.html): 125 | **Deconstructing RDMA-enabled Distributed Transactions: Hybrid is Better!** [OSDI 2018](https://dblp.org/db/conf/osdi/osdi2018.html#WeiD0C18): 233-251 126 | 127 | 42. [Arash Tavakkol](https://dblp.org/pid/44/2524.html), [Aasheesh Kolli](https://dblp.org/pid/138/4146.html), [Stanko Novakovic](https://dblp.org/pid/142/3209.html), [Kaveh Razavi](https://dblp.org/pid/121/2685.html), [Juan Gómez-Luna](https://dblp.org/pid/28/7411.html), [Hasan Hassan](https://dblp.org/pid/147/4013.html), [Claude Barthels](https://dblp.org/pid/123/3160.html), [Yaohua Wang](https://dblp.org/pid/46/8559.html), [Mohammad Sadrosadati](https://dblp.org/pid/161/0904.html), [Saugata Ghose](https://dblp.org/pid/94/7357.html), [Ankit Singla](https://dblp.org/pid/78/8146.html), [Pratap Subrahmanyam](https://dblp.org/pid/64/2373.html), [Onur Mutlu](https://dblp.org/pid/m/OnurMutlu.html): 128 | **Enabling Efficient RDMA-based Synchronous Mirroring of Persistent Memory Transactions.** [CoRR abs/1810.09360](https://dblp.org/db/journals/corr/corr1810.html#abs-1810-09360) (2018) 129 | 130 | 43. [Xiaoyi Lu](https://dblp.org/pid/27/5660.html), [Dipti Shankar](https://dblp.org/pid/153/6812.html), [Dhabaleswar K. Panda](https://dblp.org/pid/p/DhabaleswarKPanda.html): 131 | **Scalable and Distributed Key-Value Store-based Data Management Using RDMA-Memcached.** [IEEE Data Eng. Bull. 40(1)](https://dblp.org/db/journals/debu/debu40.html#LuSP17): 50-61 (2017) 132 | 133 | 44. [Duc Hai Nguyen](https://dblp.org/pid/179/8180.html), [The De Vu](https://dblp.org/pid/192/3667.html), [Duc Hieu Nguyen](https://dblp.org/pid/192/3687.html), [Minh Duc Le](https://dblp.org/pid/125/5780.html), [Tien Hai Ho](https://dblp.org/pid/192/3681.html), [Tran Vu Pham](https://dblp.org/pid/40/2289.html): 134 | **Key-Value-Links: A New Data Model for Developing Efficient RDMA-Based In-Memory Stores.** [Informatica (Slovenia) 41(2)](https://dblp.org/db/journals/informaticaSI/informaticaSI41.html#NguyenVNLHP17) (2017) 135 | 136 | 45. [Dipti Shankar](https://dblp.org/pid/153/6812.html), [Xiaoyi Lu](https://dblp.org/pid/27/5660.html), [Jithin Jose](https://dblp.org/pid/42/10334.html), [Md. Wasi-ur-Rahman](https://dblp.org/pid/15/10332.html), [Nusrat S. Islam](https://dblp.org/pid/23/10333.html), [Dhabaleswar K. Panda](https://dblp.org/pid/p/DhabaleswarKPanda.html): 137 | **Can RDMA benefit online data processing workloads on memcached and MySQL?** [ISPASS 2015](https://dblp.org/db/conf/ispass/ispass2015.html#ShankarLJWIP15): 159-160 138 | 139 | 46. 魏寒. 基于RDMA的共享缓冲区分布式数据库事务处理设计与实现[D].华东师范大学,2021.DOI:10.27149/d.cnki.ghdsu.2021.002085. 140 | 141 | 47. 黄晨晨. 基于非易失内存和RDMA高速网络的数据库索引技术研究[D].华东师范大学,2021.DOI:10.27149/d.cnki.ghdsu.2021.000466. 142 | 143 | 48. 梁戈. 基于RDMA的分布式数据库内存存储系统[D].电子科技大学,2020.DOI:10.27005/d.cnki.gdzku.2020.002791. 144 | 145 | 49. 马俊伟. 基于RDMA读优先锁机制的设计与实现[D].华中科技大学,2019.DOI:10.27157/d.cnki.ghzku.2019.000762. 146 | 147 | 50. 陈超. 基于RDMA的分布式内存数据库查询引擎[D].电子科技大学,2018. 148 | 149 | 51. 薛翔. 基于RDMA的分布式系统二级索引设计与实现[D].上海交通大学,2018.DOI:10.27307/d.cnki.gsjtu.2018.003122. 150 | 151 | 52. 陈彦哲. 基于RDMA和HTM的分布式事务系统的设计与实现[D].上海交通大学,2017. 152 | 153 | 53. 杨敏. 基于RDMA分布式key-value存储系统的设计与实现[D].国防科学技术大学,2016. 154 | 155 | 54. 杨敏. 基于RDMA分布式key-value存储系统的设计与实现[D]. 国防科学技术大学. 156 | 157 | 55. 唐文慧. 内存NoSQL系统Redis性能优化研究与实现[D]. 国防科技大学, 2017. 158 | 159 | -------------------------------------------------------------------------------- /Paper/内存数据库技术.md: -------------------------------------------------------------------------------- 1 | # In-Memory Database 2 | 3 | ## Surey 4 | 5 | + Tobias Maltenberger, Till Lehmann, Lawrence Benson, Tilmann Rabl: 6 | Evaluating In-Memory Hash Joins on Persistent Memory. EDBT 2022: 2:368-2:372 7 | + Qian Zhang, Jianhao Wei, Yiwen Xiang, Chuqiao Xiao: 8 | A Performance Evaluation of DRAM Access for In-Memory Databases. IEEE Access 9: 146454-146470 (2021) 9 | + Simone Dominico, Eduardo C. de Almeida, Marco A. Z. Alves, Jorge Augusto Meira: 10 | Performance Analysis of Array Database Systems in Non-Uniform Memory Architecture. PDP 2021: 169-176 11 | + Jian Fang, Yvo T. B. Mulder, Jan Hidders, Jinho Lee, H. Peter Hofstee: 12 | In-memory database acceleration on FPGAs: a survey. VLDB J. 29(1): 33-59 (2020) 13 | + Tiemo Bang, Ismail Oukid, Norman May, Ilia Petrov, Carsten Binnig: 14 | Robust Performance of Main Memory Data Structures by Configuration. 1651-1666 15 | + Tiago Rodrigo Kepe, Eduardo C. de Almeida, Marco A. Z. Alves: 16 | Database Processing-in-Memory: An Experimental Study. Proc. VLDB Endow. 13(3): 334-347 (2019) 17 | + Tiago Rodrigo Kepe, Eduardo C. de Almeida, Marco A. Z. Alves, Jorge Augusto Meira: 18 | Database Processing-in-Memory: A Vision. DEXA (1) 2019: 418-428 19 | + Maximilian E. Schüle, Lukas Karnowski, Josef Schmeißer, Benedikt Kleiner, Alfons Kemper, Thomas Neumann: 20 | Versioning in Main-Memory Database Systems: From MusaeusDB to TardisDB. SSDBM 2019: 169-180 21 | + Andy Pavlo: 22 | In-memory, horizontal, and transactional: the H-store OLTP DBMS project. Making Databases Work 2019: 245-251 23 | + Michael Stonebraker, Samuel Madden, Daniel J. Abadi, Stavros Harizopoulos, Nabil Hachem, Pat Helland: 24 | The end of an architectural era: it's time for a complete rewrite. Making Databases Work 2019: 463-489 25 | + Andreas Kipf, Varun Pandey, Jan Böttcher, Lucas Braun, Thomas Neumann, Alfons Kemper: 26 | Analytics on Fast Data: Main-Memory Database Systems versus Modern Streaming Systems. EDBT 2017: 49-60 27 | + Per-Åke Larson, Justin J. Levandoski: 28 | Modern Main-Memory Database Systems. Proc. VLDB Endow. 9(13): 1609-1610 (2016) 29 | + Alfons Kemper: 30 | HyPer beyond software: Exploiting modern hardware for main-memory database systems. ICDE Workshops 2016: 130 31 | + Tobias Mühlbauer, Wolf Rödiger, Andreas Kipf, Alfons Kemper, Thomas Neumann: 32 | High-Performance Main-Memory Database Systems and Modern Virtualization: Friends or Foes? DanaC@SIGMOD 2015: 4:1-4:4 33 | 34 | ## System or System 35 | 36 | + Thomas Neumann: 37 | Evolution of a Compiling Query Engine. Proc. VLDB Endow. 14(12): 3207-3210 (2021) 38 | + Prakash Jha, Navneet Gangwar, Himanshu Mani Tripathi, Tareq Bin Ahammed, Samim Jahin, Ripon Patgiri: 39 | AxomDB: A Distributed and Scalable In-memory Database System. OCIT 2021: 20-25 40 | + Colin Adams, Luis Alonso, Benjamin Atkin, John Banning, Sumeer Bhola, Rick Buskens, Ming Chen, Xi Chen, Yoo Chung, Qin Jia, Nick Sakharov, George Talbot, Nick Taylor, Adam Tart: 41 | Monarch: Google's Planet-Scale In-Memory Time Series Database. Proc. VLDB Endow. 13(12): 3181-3194 (2020) 42 | + Paul Blockhaus, David Broneske, Martin Schäler, Veit Köppen, Gunter Saake: 43 | Combining Two Worlds: MonetDB with Multi-Dimensional Index Structure Support to Efficiently Query Scientific Data. SSDBM 2020: 29:1-29:4 44 | + Johannes Pietrzyk, Dirk Habich, Patrick Damme, Erich Focht, Wolfgang Lehner: 45 | Evaluating the Vector Supercomputer SX-Aurora TSUBASA as a Co-Processor for In-Memory Database Systems. Datenbank-Spektrum 19(3): 183-197 (2019) 46 | + Prerna Budhkar, Ildar Absalyamov, Vasileios Zois, Skyler Windh, Walid A. Najjar, Vassilis J. Tsotras: 47 | Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads. ACM Trans. Archit. Code Optim. 16(2): 13:1-13:28 (2019) 48 | + Dirk Habich: 49 | MorphStore - From an Idea to a Novel In-Memory Column Store Database System. Grundlagen von Datenbanken 2019: 2 50 | + Chen Li, Bo Li, Md. Zakirul Alam Bhuiyan, Lihong Wang, Jinghui Si, Guanyu Wei, Jianxin Li: 51 | FluteDB: An efficient and scalable in-memory time series database for sensor-cloud. J. Parallel Distributed Comput. 122: 95-108 (2018) 52 | + João Oliveira, Jorge Bernardino: 53 | NewSQL Databases - MemSQL and VoltDB Experimental Evaluation. KEOD 2017: 276-281 54 | + Amir Shaikhha, Yannis Klonatos, Lionel Parreaux, Lewis Brown, Mohammad Dashti, Christoph Koch: 55 | How to Architect a Query Compiler. SIGMOD Conference 2016: 1907-1922 56 | + Juliana Hildebrandt, Dirk Habich, Patrick Damme, Wolfgang Lehner: 57 | Compression-Aware In-Memory Query Processing: Vision, System Design and Beyond. ADMS/IMDM@VLDB 2016: 40-56 58 | + Dinesh Das, Jiaqi Yan, Mohamed Zaït, Satyanarayana R. Valluri, Nirav Vyas, Ramarajan Krishnamachari, Prashant Gaharwar, Jesse Kamp, Niloy Mukherjee: 59 | Query Optimization in Oracle 12c Database In-Memory. Proc. VLDB Endow. 8(12): 1770-1781 (2015) 60 | + Michael Stonebraker, Ariel Weisberg: 61 | The VoltDB Main Memory DBMS. IEEE Data Eng. Bull. 36(2): 21-27 (2013) 62 | + Alfons Kemper, Thomas Neumann: 63 | HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots. ICDE 2011: 195-206 64 | + 陆鑫, 王艳蓉, 孙超,等. 一种高性能多模式的内存数据库系统. 计算机应用与软件, 2019, 36(1):5. 65 | 66 | ## Index 67 | 68 | + Gang Wu, Yidong Song, Guodong Zhao, Wei Sun, Donghong Han, Baiyou Qiao, Guoren Wang, Ye Yuan: 69 | Cracking in-memory database index: A case study for Adaptive Radix Tree index. Inf. Syst. 104: 101913 (2022) 70 | + Niv Dayan, Moshe Twitto: 71 | Chucky: A Succinct Cuckoo Filter for LSM-Tree. SIGMOD Conference 2021: 365-378 72 | + Moshik Hershcovitch, Artem Khyzha, Daniel G. Waddington, Adam Morrison: 73 | Elastic Indexes: Dynamic Space vs. Query Efficiency Tuning for In-Memory Database Indexing. EDBT 2022: 2:312-2:324 74 | + Marcel Weisgut:Experimental Index Evaluation for Partial Indexes in Horizontally Partitioned In-Memory Databases. GvDB 2021 75 | + Huanchen Zhang, Xiaoxuan Liu, David G. Andersen, Michael Kaminsky, Kimberly Keeton, Andrew Pavlo: 76 | Order-Preserving Key Compression for In-Memory Search Trees. 1601-1615 77 | + Hitoshi Mitake, Hiroshi Yamada, Tatsuo Nakajima: 78 | A Highly Scalable Index Structure for Multicore In-Memory Database Systems. IDC 2019: 210-217 79 | + Gang Wu, Yidong Song, Guodong Zhao, Wei Sun, Donghong Han, Baiyou Qiao, Guoren Wang, Ye Yuan: 80 | Cracking In-Memory Database Index A Case Study for Adaptive Radix Tree Index. CoRR abs/1911.11387 (2019) 81 | + Robert Binna, Eva Zangerle, Martin Pichl, Günther Specht, Viktor Leis: 82 | HOT: A Height Optimized Trie Index for Main-Memory Database Systems. SIGMOD Conference 2018: 521-534 83 | + Aditya Gurajada, Dheren Gala, Fei Zhou, Amit Pathak, Zhan-Feng Ma: 84 | BTrim - Hybrid In-Memory Database Architecture for Extreme Transaction Processing in VLDBs. Proc. VLDB Endow. 11(12): 1889-1901 (2018) 85 | + Viktor Leis, Florian Scheibner, Alfons Kemper, Thomas Neumann: 86 | The ART of practical synchronization. DaMoN 2016: 3:1-3:8 87 | + Viktor Leis, Alfons Kemper, Thomas Neumann: 88 | The adaptive radix tree: ARTful indexing for main-memory databases. ICDE 2013: 38-49 89 | + Justin J. Levandoski, David B. Lomet, Sudipta Sengupta: 90 | The Bw-Tree: A B-tree for new hardware platforms. ICDE 2013: 302-313 91 | 92 | ## Query 93 | 94 | + Ben Perach, Ronny Ronen, Benny Kimelfeld, Shahar Kvatinsky: 95 | PIMDB: Understanding Bulk-Bitwise Processing In-Memory Through Database Analytics. CoRR abs/2203.10486 (2022) 96 | + Yesdaulet Izenov, Asoke Datta, Florin Rusu, Jun Hyung Shin: 97 | COMPASS: Online Sketch-based Query Optimization for In-Memory Databases. SIGMOD Conference 2021: 804-816 98 | + Iason Giannopoulos, Abhairaj Singh, Manuel Le Gallo, Vara Prasad Jonnalagadda, Said Hamdioui, Abu Sebastian: 99 | In-Memory Database Query. Adv. Intell. Syst. 2(12): 2000141 (2020) 100 | + Mingjie Tang, Yongyang Yu, Ahmed R. Mahmood, Qutaibah M. Malluhi, Mourad Ouzzani, Walid G. Aref: 101 | LocationSpark: In-memory Distributed Spatial Query Processing and Optimization. Frontiers Big Data 3: 30 (2020) 102 | + Mayuresh Kunjir, Shivnath Babu: 103 | Black or White? How to Develop an AutoTuner for Memory-based Analytics. 1667-1683 104 | + Polychronis Velentzas, Michael Vassilakopoulos, Antonio Corral: 105 | In-memory k Nearest Neighbor GPU-based Query Processing. GISTAM 2020: 310-317 106 | + Mustafa Cavus, Mohammed Shatnawi, Resit Sendag, Augustus K. Uht: 107 | Exploring Prefetching, Pre-Execution and Branch Outcome Streaming for In-Memory Database Lookups. IEEE Comput. Archit. Lett. 19(1): 5-8 (2020) 108 | + David Broneske: 109 | Accelerating mono and multi-column selection predicates in modern main-memory database systems. Otto-von-Guericke University Magdeburg, Germany, 2019 110 | + Cagri Balkesen, Nitin Kunal, Georgios Giannikis, Pit Fender, Seema Sundara, Felix Schmidt, Jarod Wen, Sandeep R. Agrawal, Arun Raghavan, Venkatanathan Varadarajan, Anand Viswanathan, Balakrishnan Chandrasekaran, Sam Idicula, Nipun Agarwal, Eric Sedlar: 111 | RAPID: In-Memory Analytical Query Processing Engine with Extreme Performance per Watt. SIGMOD Conference 2018: 1407-1419 112 | + Accelerating mono and multi-column selection predicates in modern main-memory database systems. Otto-von-Guericke University Magdeburg, Germany, 2019 113 | + Mahendra Pratap Singh, Shamik Sural, Jaideep Vaidya, Vijayalakshmi Atluri: 114 | Managing attribute-based access control policies in a unified framework using data warehousing and in-memory database. Comput. Secur. 86: 183-205 (2019) 115 | + Keven Richly: 116 | Optimized Spatio-Temporal Data Structures for Hybrid Transactional and Analytical Workloads on Columnar In-Memory Databases. PhD@VLDB 2019 117 | + Jun Hyung Shin, Florin Rusu, Alex Suhan: 118 | Exact Selectivity Computation for Modern In-Memory Database Query Optimization. CoRR abs/1901.01488 (2019) 119 | + Timo Kersten, Viktor Leis, Alfons Kemper, Thomas Neumann, Andrew Pavlo, Peter A. Boncz: 120 | Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask. Proc. VLDB Endow. 11(13): 2209-2222 (2018) 121 | + Pietro Michiardi, Damiano Carra, Sara Migliorini: 122 | In-memory Caching for Multi-query Optimization of Data-intensive Scalable Computing Workloads. EDBT/ICDT Workshops 2019 123 | + Demian Hespe, Martin Weidner, Jonathan Dees, Peter Sanders: 124 | Fast OLAP Query Execution in Main Memory on Large Data in a Cluster. CoRR abs/1709.05183 (2017) 125 | + Yin-Fu Huang, Wei-Cheng Chen: 126 | Parallel Query on the In-Memory Database in a CUDA Platform. 3PGCIC 2015: 236-243 127 | + Orestis Polychroniou, Arun Raghavan, Kenneth A. Ross: 128 | Rethinking SIMD Vectorization for In-Memory Databases. SIGMOD Conference 2015: 1493-1508 129 | + Darius Sidlauskas, Simonas Saltenis, Christian S. Jensen: 130 | Processing of extreme moving-object update and query workloads in main memory. VLDB J. 23(5): 817-841 (2014) 131 | 132 | ## Fast Scan and Join 133 | 134 | + Maximilian Bandle, Jana Giceva, Thomas Neumann: 135 | To Partition, or Not to Partition, That is the Join Question in a Real System. SIGMOD Conference 2021: 168-180 136 | + Peyman Behzadnia: 137 | Shared-Memory Parallel Hash-Based Stream Join in Continuous Data Streams. DEXA (2) 2021: 313-318 138 | + Xing Wei, Huiqi Hu, Xuan Zhou, Aoying Zhou: 139 | A Chunk-Based Hash Table Caching Method for In-Memory Hash Joins. WISE (2) 2020: 376-389 140 | + Zhuhe Fang, Zeyu He, Jiajia Chu, Chuliang Weng: 141 | SIMD Accelerates the Probe Phase of Star Joins in Main Memory Databases. DASFAA (3) 2019: 476-480 142 | + Puya Memarzia, Suprio Ray, Virendra C. Bhavsar: 143 | On Improving Data Skew Resilience In Main-memory Hash Joins. IDEAS 2018: 226-235 144 | + + Stefan Schuh, Xiao Chen, Jens Dittrich: 145 | An Experimental Comparison of Thirteen Relational Equi-Joins in Main Memory. SIGMOD Conference 2016: 1961-1976 146 | + Michael S. Kester, Manos Athanassoulis, Stratos Idreos: 147 | Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe? SIGMOD Conference 2017: 715-730 148 | + Yinan Li, Jignesh M. Patel: 149 | BitWeaving: fast scans for main memory data processing. SIGMOD Conference 2013: 289-300 150 | + Cagri Balkesen, Jens Teubner, Gustavo Alonso, M. Tamer Özsu: 151 | Main-memory hash joins on multi-core CPUs: Tuning to the underlying hardware. ICDE 2013: 362-37 152 | + 罗永平, 金培权. NVM+DRAM混合内存架构下的连接算法优化. 计算机学报, 2020, 43(6):17. 153 | + 张宇, 张延松. 向量分组聚集计算技术研究. 计算机工程与应用, 2021, 57(11):11. 154 | + 杨帆, 高国静, 张怡锋. 并行计算框架的内存优化算法研究. 信息技术, 2020, 44(8):5. 155 | + 张延松, 张宇, 王珊. 基于向量引用Platform-Oblivious内存连接优化技术. 软件学报, 2018, 29(3):13. 156 | + 张延松, 张宇, 王珊. 一种基于向量索引的内存OLAP星型连接加速新技术. 计算机学报, 2019, 42(8):18. 157 | 158 | ## Memory Management 159 | 160 | + Motoshi Miyamoto, Ryota Kawashima, Hiroshi Matsuo: 161 | Transparent Relational Database Caching Based on Storage Engines Using In-memory Database. CANDAR (Workshops) 2021: 444-448 162 | + Kwangjin Lee, Hwajung Kim, Heon Y. Yeom: 163 | Validity Tracking Based Log Management for In-Memory Databases. IEEE Access 9: 111493-111504 (2021) 164 | + Viktor Leis, Michael Haubenschild, Alfons Kemper, Thomas Neumann: 165 | LeanStore: In-Memory Data Management beyond Main Memory. ICDE 2018: 185-196 166 | + Kangnyeon Kim, Tianzheng Wang, Ryan Johnson, Ippokratis Pandis: 167 | ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads. SIGMOD Conference 2016: 1675-1687 168 | + Goetz Graefe, Haris Volos, Hideaki Kimura, Harumi A. Kuno, Joseph Tucek, Mark Lillibridge, Alistair C. Veitch: 169 | In-Memory Performance for Big Data. Proc. VLDB Endow. 8(1): 37-48 (2014) 170 | + Viktor Leis, Peter A. Boncz, Alfons Kemper, Thomas Neumann: 171 | Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age. SIGMOD Conference 2014: 743-754 172 | + Justin J. Levandoski, David B. Lomet, Sudipta Sengupta: 173 | LLAMA: A Cache/Storage Subsystem for Modern Hardware. Proc. VLDB Endow. 6(10): 877-888 (2013) 174 | + Justin A. DeBrabant, Andrew Pavlo, Stephen Tu, Michael Stonebraker, Stanley B. Zdonik: 175 | Anti-Caching: A New Approach to Database Management System Architecture. Proc. 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[Yingjun Wu](https://dblp.org/pid/07/11533.html), [Wentian Guo](https://dblp.org/pid/179/2634.html), [Chee-Yong Chan](https://dblp.org/pid/c/CheeYongChan.html), [Kian-Lee Tan](https://dblp.org/pid/t/KianLeeTan.html)![0000-0001-9315-4057](https://dblp.org/img/orcid-mark.12x12.png): 127 | **Fast Failure Recovery for Main-Memory DBMSs on Multicores.** [SIGMOD Conference 2017](https://dblp.org/db/conf/sigmod/sigmod2017.html#WuGCT17): 267-281 128 | 129 | 44. [Zhaoguo Wang](https://dblp.org/pid/50/9117.html), [Shuai Mu](https://dblp.org/pid/98/7677-1.html), [Yang Cui](https://dblp.org/pid/73/2753.html), [Han Yi](https://dblp.org/pid/11/5634.html), [Haibo Chen](https://dblp.org/pid/31/6601-1.html), [Jinyang Li](https://dblp.org/pid/79/572-1.html): 130 | **Scaling Multicore Databases via Constrained Parallel Execution.** [SIGMOD Conference 2016](https://dblp.org/db/conf/sigmod/sigmod2016.html#WangMCYCL16): 1643-1658 131 | 132 | 45. 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[Yihe Huang](https://dblp.org/pid/178/4804.html), [William Qian](https://dblp.org/pid/216/3374-1.html)![0000-0002-0856-2134](https://dblp.org/img/orcid-mark.12x12.png), [Eddie Kohler](https://dblp.org/pid/78/5231.html), [Barbara Liskov](https://dblp.org/pid/l/BarbaraLiskov.html), [Liuba Shrira](https://dblp.org/pid/s/LiubaShrira.html): 139 | **Opportunities for Optimism in Contended Main-Memory Multicore Transactions.** [Proc. VLDB Endow. 13(5)](https://dblp.org/db/journals/pvldb/pvldb13.html#HuangQKLS20): 629-642 (2020) 140 | 141 | 48. [Lucas Lersch](https://dblp.org/pid/180/3171.html), [Ivan Schreter](https://dblp.org/pid/177/6046.html), [Ismail Oukid](https://dblp.org/pid/135/3665.html), [Wolfgang Lehner](https://dblp.org/pid/l/WLehner.html): 142 | **Enabling Low Tail Latency on Multicore Key-Value Stores.** [Proc. VLDB Endow. 13(7)](https://dblp.org/db/journals/pvldb/pvldb13.html#LerschSOL20): 1091-1104 (2020) 143 | 144 | 49. [Hyungsoo Jung](https://dblp.org/pid/45/1270.html), [Hyuck Han](https://dblp.org/pid/17/5450.html), [Sooyong Kang](https://dblp.org/pid/19/6501.html): 145 | **Scalable Database Logging for Multicores.** [Proc. VLDB Endow. 11(2)](https://dblp.org/db/journals/pvldb/pvldb11.html#JungHK17): 135-148 (2017) 146 | 147 | 50. [Jana Giceva](https://dblp.org/pid/32/7263.html), [Gustavo Alonso](https://dblp.org/pid/a/GustavoAlonso.html), [Timothy Roscoe](https://dblp.org/pid/r/TimothyRoscoe.html), [Tim Harris](https://dblp.org/pid/61/3834.html): 148 | **Deployment of Query Plans on Multicores.** [Proc. VLDB Endow. 8(3)](https://dblp.org/db/journals/pvldb/pvldb8.html#GicevaARH14): 233-244 (2014) 149 | -------------------------------------------------------------------------------- /Paper/数据库一体机技术.md: -------------------------------------------------------------------------------- 1 | # 数据库一体机技术 2 | 3 | ## Alibaba PolarDB 4 | 5 | - White Paper：https://help.aliyun.com/document_detail/68747.html 6 | 7 | - Wei Cao, Yingqiang Zhang, Xinjun Yang, Feifei Li, Sheng Wang, Qingda Hu, Xuntao Cheng, Zongzhi Chen, Zhenjun Liu, Jing Fang, Bo Wang, Yuhui Wang, Haiqing Sun, Ze Yang, Zhushi Cheng, Sen Chen, Jian Wu, Wei Hu, Jianwei Zhao, Yusong Gao, Songlu Cai, Yunyang Zhang, Jiawang Tong:PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers. SIGMOD Conference 2021: 2477-2489 8 | - Wei Cao, Yang Liu, Zhushi Cheng, Ning Zheng, Wei Li, Wenjie Wu, Linqiang Ouyang, Peng Wang, Yijing Wang, Ray Kuan, Zhenjun Liu, Feng Zhu, Tong Zhang: POLARDB Meets Computational Storage: Efficiently Support Analytical Workloads in Cloud-Native Relational Database. FAST 2020: 29-41 9 | 10 | ## Oracle Exadata 11 | 12 | - White Paper:Carl W. Olofson: A Revolution in Data Management: Oracle Exadata X8M. 13 | - Oracla Exadata. https://www.oracle.com/technetwork/database/exadata/exadata-storage-technical-overview-128045.pdf . 14 | 15 | ## Teradata 16 | 17 | - www.teradata.com 18 | 19 | - John Catozzi, Sorana Rabinovici: Operating System Extensions for the Teradata Parallel VLDB. VLDB 2001: 679-682 20 | 21 | ## Vertica 22 | 23 | - https://www.vertica.com/docs/11.1.x/HTML/Content/Home.htm 24 | - Andrew Lamb, Matt Fuller, Ramakrishna Varadarajan, Nga Tran, Ben Vandiver, Lyric Doshi, Chuck Bear: The Vertica Analytic Database: C-Store 7 Years Later . Proc. VLDB Endow. 5(12): 1790-1801 (2012) 25 | 26 | ## IBM Netezza 27 | 28 | - **White Paper： **https://www.ibm.com/support/pages/system/files/support/swg/swgdocs.nsf/0/5bcf5cb742567a9985257840005c4795/$FILE/IBM%20Industry%20Data%20Models%20-%20Netezza%20Whitepaper.pdf 29 | - Malcolm Singh, Ben Leonhardi: Introduction to the IBM Netezza warehouse appliance. CASCON 2011: 385-386 30 | 31 | ## EMC GreenPlum 32 | 33 | - https://www.slideshare.net/emcacademics/h8869-emcgreenplumdcaoraclegdwwp-12045737 34 | - **White Paper:** https://fdocuments.in/document/white-paper-emc-greenplum-data-computing-appliance-enhances-emc-its-global-data-warehouse.html 35 | - Zhenghua Lyu, Huan Hubert Zhang, Gang Xiong, Gang Guo, Haozhou Wang, Jinbao Chen, Asim Praveen, Yu Yang, Xiaoming Gao, Alexandra Wang, Wen Lin, Ashwin Agrawal, Junfeng Yang, Hao Wu, Xiaoliang Li, Feng Guo, Jiang Wu, Jesse Zhang, Venkatesh Raghavan: Greenplum: A Hybrid Database for Transactional and Analytical Workloads. SIGMOD Conference 2021: 2530-2542 36 | -------------------------------------------------------------------------------- /Paper/新型存储器技术.md: -------------------------------------------------------------------------------- 1 | # 新型存储器技术 2 | 3 | ## Survey 4 | 5 | + 马竹琳, 李心池, 诸葛晴凤,等. 面向非易失性存储器的多表连接写操作的优化研究. 计算机学报, 2019, 42(11):12. 6 | + 朱阅岸, 简怀兵, 龙永超,等. 构建新型高性能与高可用的键值数据库系统. 软件学报, 2021, 32(10):16. 7 | 8 | ## MRAM(STTRAM) 9 | 10 | + Xinpeng Jiang, Junlin Bao, Li Zhang, Lei Bai: 11 | A novel dual-reference sensing scheme for computing in memory within STT-MRAM. Microelectron. J. 121: 105355 (2022) 12 | + Elham Cheshmikhani, Hamed Farbeh, Hossein Asadi: 13 | A System-Level Framework for Analytical and Empirical Reliability Exploration of STT-MRAM Caches. CoRR abs/2201.02855 (2022) 14 | + Mahdi Talebi, Arash Salahvarzi, Amir Mahdi Hosseini Monazzah, Kevin Skadron, Mahdi Fazeli: 15 | ROCKY: A Robust Hybrid On-Chip Memory Kit for the Processors With STT-MRAM Cache Technology. IEEE Trans. Computers 70(12): 2198-2210 (2021) 16 | + Xin Ma, Liang Chang, Shuangchen Li, Lei Deng, Yufei Ding, Yuan Xie: 17 | In-memory multiplication engine with SOT-MRAM based stochastic computing. CoRR abs/1809.08358 (2018) 18 | + Mostafa Rizk, Jean-Philippe Diguet, Naoya Onizawa, Amer Baghdadi, Martha Johanna Sepúlveda, Y. Akgul, Vincent Gripon, Takahiro Hanyu: 19 | NoC-MRAM architecture for memory-based computing: Database-search case study. NEWCAS 2017: 309-312 20 | + Jie Zhang, Miryeong Kwon, Chanyoung Park, Myoungsoo Jung, Songkuk Kim: 21 | ROSS: A Design of Read-Oriented STT-MRAM Storage for Energy-Efficient Non-Uniform Cache Architecture. INFLOW@OSDI 2016 22 | 23 | ## RRAM 24 | 25 | + Eduardo Esmanhotto, Tifenn Hirtzlin, Niccolo Castellani, S. Martin, Bastien Giraud, François Andrieu, Jean-Francois Nodin, Damien Querlioz, Jean-Michel Portal, Elisa Vianello: 26 | Experimental demonstration of Single-Level and Multi-Level-Cell RRAM-based In-Memory Computing with up to 16 parallel operations. CoRR abs/2203.01680 (2022) 27 | + Alexander Serb, Ali Khiat, Themis Prodromakis: 28 | Practical demonstration of a RRAM memory fuse. Int. J. Circuit Theory Appl. 49(8): 2363-2372 (2021) 29 | + Yuliang Sun, Yu Wang, Huazhong Yang: 30 | Bidirectional Database Storage and SQL Query Exploiting RRAM-Based Process-in-Memory Structure. ACM Trans. Storage 14(1): 8:1-8:19 (2018) 31 | + 杜娇, 钱育蓉, 侯海耀,等. 基于RRAM的混合存储模型. 计算机工程与设计, 2018, 39(10):5. 32 | 33 | ## NAND Flash 34 | 35 | + Hyun-Jeong Shim, Xian-Shu Li, Su-Kyung Yoon, Shin-Dug Kim: 36 | Locality aware management on NAND flash-based main memory for in-memory database systems. EDB 2016: 90-94 37 | 38 | ## ReRAM 39 | 40 | + Bertrand Cambou, Donald Telesca, Sareh Assiri, Michael Garrett, Saloni Jain, Michael Partridge: 41 | TRNGs from Pre-Formed ReRAM Arrays. Cryptogr. 5(1): 8 (2021) 42 | 43 | ## NVRAM 44 | 45 | + Dharamjeet, Tseng-Yi Chen, Yuan-Hao Chang, Chun-Feng Wu, Chi-Heng Lee, Wei-Kuan Shih: 46 | Beyond Write-Reduction Consideration: A Wear-Leveling-Enabled B⁺-Tree Indexing Scheme Over an NVRAM-Based Architecture. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 40(12): 2455-2466 (2021) 47 | + I-Ju Wang, Yu-Pei Liang, Tseng-Yi Chen, Yuan-Hao Chang, Bo-Jun Chen, Hsin-Wen Wei, Wei-Kuan Shih: 48 | Enabling Write-Reduction Multiversion Scheme With Efficient Dual-Range Query Over NVRAM. IEEE Trans. Very Large Scale Integr. Syst. 29(6): 1244-1256 (2021) 49 | + Zhu Mei, Lin Wang, Jun Yu, Hengmao Pang, Haiyang Chen, Luwei Zhang, Jinlong Wu, Mingjie Xu, Lin Qian: 50 | Research on write optimization of NVRAM memory management system based on decision tree and LSM-Tree. CIPAE 2020: 89-95 51 | + Wook-Hee Kim, Jihye Seo, Jinwoong Kim, Beomseok Nam: 52 | clfB-tree: Cacheline Friendly Persistent B-tree for NVRAM. ACM Trans. Storage 14(1): 5:1-5:17 (2018) 53 | + Masab Ahmad, Halit Dogan, Fabio Checconi, Xinyu Que, Daniele Buono, Omer Khan: 54 | Software-Hardware Managed Last-level Cache Allocation Scheme for Large-Scale NVRAM-Based Multicores Executing Parallel Data Analytics Applications. IPDPS 2018: 316-325 55 | + Lucas Lersch, Ismail Oukid, Wolfgang Lehner, Ivan Schreter: 56 | An analysis of LSM caching in NVRAM. DaMoN 2017: 9:1-9:5 57 | 58 | -------------------------------------------------------------------------------- /Patent/Actian Vector专利.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Patent/Altibase专利.md: -------------------------------------------------------------------------------- 1 | ### Altibase 2 | + Heterogeneous database storage unified interface and database access method 3 | -------------------------------------------------------------------------------- /Patent/ClickHouse 华为专利.md: -------------------------------------------------------------------------------- 1 | ### 华为数据库专利 2 | 3 | + Column-oriented database processing method and processing device 4 | + Data storage method and apparatus 5 | + Full and incremental synchronization method, device and storage medium for Clickhouse 6 | + Method and system for adaptively building and updating column store database from row store database based on query demands 7 | + Query optimization adaptive to system memory load for parallel database systems 8 | + Systems and methods for database management using append-only storage devices 9 | + The method and system of column storage database is adaptively built from the row data storage storehouse of current time based on query demand 10 | + Query support method and system based on stored data in ClickHouse database 11 | -------------------------------------------------------------------------------- /Patent/EXASol 专利.md: -------------------------------------------------------------------------------- 1 | ### EXASol 2 | + Fast OLAP Query Execution in Main Memory on Large Data in a Cluster 3 | -------------------------------------------------------------------------------- /Patent/Oracle 专利.md: -------------------------------------------------------------------------------- 1 | ### Oracle 2 | 3 | + A kind of compressed data synchronous method and device based on oracle database 4 | + Multi-version concurrency control on in-memory snapshot store of oracle in-memory database 5 | -------------------------------------------------------------------------------- /Patent/SQL Server专利.md: -------------------------------------------------------------------------------- 1 | ### SQL Server 2 | + A kind of storage method and storage system of SQL SERVER database updates 3 | + Multi-step query execution in sql server 4 | -------------------------------------------------------------------------------- /Patent/alibaba analyticdb专利.md: -------------------------------------------------------------------------------- 1 | ### alibaba analyticdb 2 | 3 | + Tiered data storage and tiered query method and apparatus 4 | + The storage method and device of database data 5 | + Row-column formatting for relational databases 6 | + Database index creation method and device 7 | -------------------------------------------------------------------------------- /Patent/数据库专利汇总（按技术分类）.md: -------------------------------------------------------------------------------- 1 | ### GPU数据库 2 | 3 | + Data query server, system and method based on online analytical processing (OLAP) 4 | + DATABASE ACCELERATION USING GPU AND MULTICORE CPU SYSTEMS AND METHODS 5 | + GPU and CPU heterogeneous acceleration method for database 6 | + GPU ENABLED DATABASE SYSTEMS 7 | + GPU-BASED DATA JOIN 8 | + METHOD AND APPARATUS FOR QUERYING DATA IN DATABASE 9 | + OLAP system based on GPU 10 | + OLAP star connection query optimizing method under CPU and GPU mixing framework 11 | + Vectorization query processing method for CPU and GPU platforms 12 | + SELECTIVE UTILIZATION OF GRAPHICS PROCESSING UNIT (GPU) BASED ACCELERATION IN DATABASE MANAGEMENT 13 | + OLAP system based on GPU 14 | + System and method for the parallel execution of database queries over CPUs and multi core processors 15 | + The star-like Connection inquiring optimization method of OLAP under GPU and CPU mixed architecture 16 | + Selective utilization of graphics processing unit (GPU) based acceleration in database management 17 | + System for processing multiple queries using gpu 18 | + Self-adaptive database hybrid query method 19 | 20 | ### FPGA数据库 21 | 22 | + FPGA-based Join algorithm implementation method, system and device and medium 23 | + Hash join acceleration method and system based on BRAM in FPGA chip 24 | + Hash join operator acceleration method and system based on FPGA-DDR 25 | 26 | ### 连接算子优化 27 | 28 | + Hash-join in parallel computation environments 29 | + Join operations in hybrid main memory systems 30 | + Optimizing skewed joins in big data 31 | + Probing a hash table using vectorized instructions 32 | + Splitting of a join operation to allow parallelization 33 | + Techniques for facilitating the joining of datasets 34 | + HASH MULTI-TABLE JOIN IMPLEMENTATION METHOD BASED ON GROUPING VECTOR 35 | + Query optimization method based on join index in data warehouse 36 | 37 | 38 | 39 | ### 选择&&聚集算子优化 40 | 41 | 42 | ### 数据存储优化 43 | 44 | + Column-oriented database processing method and processing device 45 | + Data storage method and apparatus 46 | + The method and system of column storage database is adaptively built from the row data storage storehouse of current time based on query demand 47 | + Full and incremental synchronization method, device and storage medium for Clickhouse 48 | + Method and system for adaptively building and updating column store database from row store database based on query demands 49 | + Query support method and system based on stored data in ClickHouse database 50 | + A kind of compressed data synchronous method and device based on oracle database 51 | + A kind of storage method and storage system of SQL SERVER database updates 52 | + Heterogeneous database storage unified interface and database access method 53 | + Tiered data storage and tiered query method and apparatus 54 | + Storage system of database all-in-one machine and data request processing method and device 55 | + Multi-version concurrency control on in-memory snapshot store of oracle in-memory database 56 | + N-bit compressed versioned column data array for in-memory columnar stores 57 | + In-memory row storage durability 58 | + Row-column formatting for relational databases 59 | + The storage method and device of database data 60 | 61 | ### 查询处理优化 62 | 63 | + Query optimization adaptive to system memory load for parallel database systems 64 | + Efficient query processing in columnar databases using bloom filters 65 | + Processing query to graph database 66 | + System and method for improving a query response rate by managing a column-based store in a row-based database 67 | + Multi-step query execution in sql server 68 | + Fast OLAP Query Execution in Main Memory on Large Data in a Cluster 69 | + Optimization of continuous queries in hybrid database and stream processing systems 70 | + Memory data warehouse query processing implementation method for database integrated machine 71 | + Mixed OLAP (on-line analytical processing) inquiring treating method adapting coprocessor 72 | + OLAP CUBE OPTIMIZATION USING WEIGHTINGS 73 | + SYSTEM FOR PROCESSING MULTIPLE QUERIES USING GPU 74 | + Multi-dimensional query method of satellite remote sensing data on heterogeneous computing platform 75 | + Aggregate query-caching in databases architectures with a differential buffer and a main store 76 | + Query consolidation for retrieving data from an OLAP cube 77 | 78 | 79 | 80 | 81 | ### 其它 82 | 83 | + Database system with methodology for generating bushy nested loop join trees 84 | + Executing database queries using multiple processors 85 | + Highly available main memory database system, operating method and uses thereof 86 | + Join operation partitioning 87 | + Joining tables in multiple heterogeneous distributed databases 88 | + Materializing expressions within in-memory virtual column units to accelerate analytic queries 89 | + Method and apparatus for processing query based on heterogeneous computing device 90 | + Methods and systems for hardware acceleration of streamed database operations and queries based on multiple hardware accelerators 91 | + Using shared dictionaries on join columns to improve performance of joins in relational databases 92 | + 一种基于RDMA的分布式内存数据库查询引擎系统 93 | + A kind of single-table multi-column sequence storage method for a column database 94 | + Systems and methods for database management using append-only storage devices 95 | + In-memory database system providing lock-free read and write operations for OLAP and OLTP transactions 96 | + Combined Row And Columnar Storage For In-Memory Databases For OLTP And Analytics Workloads 97 | + Mechanism to run OLTP workload on in-memory database under memory pressure 98 | + In-memory database system 99 | + Processing database queries using format conversion 100 | + Data processing method and device for online analytical processing engine and equipment 101 | + Systems and methods for managing a highly available distributed hybrid transactional and analytical database 102 | + Materializing internal computations in-memory to improve query performance 103 | + Forced ordering of a dictionary storing row identifier values 104 | + Main-memory database checkpointing 105 | + Set-oriented locking based on in-memory bitmaps for a column-oriented database 106 | + Classification of data in main memory database systems 107 | + Efficient hybrid parallelization for in-memory scans 108 | + Versioned insert only hash table for in-memory columnar stores 109 | + Method, system, and program for specifying multidimensional calculations for a relational OLAP engine 110 | + Method, apparatus and program storage device for optimizing a data warehouse model and operation 111 | + Data processing systems and methods 112 | + Mapping of an RDBMS schema onto a multidimensional data model 113 | + OLAP query processing method oriented to database and HADOOP hybrid platform 114 | + OLAP query generation engine 115 | + Improving performance of database queries 116 | + OLAP (On Line Analytical Processing) inquiry processing method facing database and Hadoop mixing platform 117 | + Online analytical processing (OLAP) 118 | + Database index creation method and device 119 | 120 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CCFA-DB 2 | 3 | 4 | [AI4DB][ai4db] 5 | 6 | [ai4db]:https://github.com/LumingSun/ML4DB-paper-list 7 | 8 | [CMU 15-445/645][cmu15445] 9 | 10 | [cmu15445]:https://15445.courses.cs.cmu.edu/fall2022/schedule.html 11 | 12 | [CMU 15-721][cmu15721] 13 | 14 | [cmu15721]:https://15721.courses.cs.cmu.edu/spring2023/schedule.html 15 | -------------------------------------------------------------------------------- /简单操作指南.md: -------------------------------------------------------------------------------- 1 | ##操作指南 2 | 3 | 1.从master分支克隆项目 4 | 5 | git clone git@github.com:CSLiuPeng/NewHardware_DBMS_PaperList.git 6 | 7 | 2.查看分支 8 | 9 | git branch 10 | 11 | 3.切换到Dev分支，在平时开发过程中master作为主要分支，一般不直接在master上开发 12 | 13 | git checkout dev 14 | 15 | 4.在开发分支上拉取代码 16 | 17 | git pull 18 | 19 | 5.提交Dev分支上的代码 20 | 21 | git add . 22 | 23 | git commit -m "操作说明" 24 | 25 | 6.将代码提交到远程仓库 26 | 27 | git push origin dev 28 | 29 | 7.将分支切换到master上 30 | 31 | git checkout master 32 | 33 | 8.将Dev分支上的代码合并到master分支 34 | 35 | git merge dev 36 | 37 | 9.推送到将Dev分支代码推送到master分支 38 | 39 | git push origin master --------------------------------------------------------------------------------