├── CONTRIBUTING.md ├── DEBUGGING.md ├── LICENSE ├── MAINTAINERS.md ├── README.md ├── configuration ├── cpd_dsx_config.json └── node-red.json ├── data └── sample_sensordata2016_1s3dys.csv ├── doc └── source │ └── images │ ├── cpd_arch_flow.png │ ├── cpd_bmx_addIOT.png │ ├── cpd_bmx_addObjectStorage.png │ ├── cpd_bmx_db2ws_createTable.png │ ├── cpd_bmx_import_nodered_flow.png │ ├── cpd_bmx_nodered_addNewNode.png │ ├── cpd_bmx_nodered_addNewNode1.png │ ├── cpd_bmx_nodered_create.png │ ├── cpd_bmx_nodered_deploy.png │ ├── cpd_bmx_nodered_menu.png │ ├── cpd_bmx_objstorage_createContainer.png │ ├── cpd_bmx_objstorage_menu.png │ ├── cpd_box_plot.png │ ├── cpd_connreaddata_fromdashdb.png │ ├── cpd_create_rsparknotebook.png │ ├── cpd_dashdb_connection.PNG │ ├── cpd_dataloadcsvtodb2.png │ ├── cpd_dataservices_createmenu.png │ ├── cpd_db2_ondsx.png │ ├── cpd_db2whs_onbluemix.png │ ├── cpd_dsx_createservice.png │ ├── cpd_dsx_menu.png │ ├── cpd_dsxconfig_json.png │ ├── cpd_dsxconfig_jsonread.png │ ├── cpd_dsxconfig_setfilename.png │ ├── cpd_dsxconfigure_params.png │ ├── cpd_dsxjson_creds.png │ ├── cpd_import_db2data.png │ ├── cpd_iot_nodered_flow.png │ ├── cpd_line_plot.png │ ├── cpd_mysql_connection.png │ ├── cpd_mysql_onbluemix.png │ ├── cpd_notebook_results.png │ ├── cpd_readdata_fromdashdb.png │ ├── cpd_upload_datafile.png │ └── delete └── notebooks └── watson_changepoint_detection.ipynb /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing 2 | 3 | This is an open source project, and we appreciate your help! 4 | 5 | We use the GitHub issue tracker to discuss new features and non-trivial bugs. 6 | 7 | In addition to the issue tracker, [#journeys on 8 | Slack](https://dwopen.slack.com) is the best way to get into contact with the 9 | project's maintainers. 10 | 11 | To contribute code, documentation, or tests, please submit a pull request to 12 | the GitHub repository. Generally, we expect two maintainers to review your pull 13 | request before it is approved for merging. For more details, see the 14 | [MAINTAINERS](MAINTAINERS.md) page. 15 | -------------------------------------------------------------------------------- /DEBUGGING.md: -------------------------------------------------------------------------------- 1 | Troubleshooting 2 | =============== 3 | 4 | Jupyter Notebooks 5 | ----------------- 6 | 7 | * While creating the Notebook make sure you chose R, Spark as the environment. 8 | The default will be Python, pay special attention to change this. 9 | * Make sure the install.packages() ran correctly. You might need to restart the 10 | kernel and run the cells from the top after the pip install runs the first 11 | time. 12 | * Many of the cells rely on variables that are set in earlier cells. Some of 13 | these are cleared in later cells. Start over at the top when troubleshooting. 14 | * Many of the cells rely on service credentials from Bluemix that are set in 15 | earlier cells. Make sure to add your service credentials correctly. 16 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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While all work can therefore be focused on the master branch, the 11 | quality of this branch should never be compromised. 12 | 13 | The remainder of this document details how to merge pull requests to the 14 | repositories. 15 | 16 | ## Merge approval 17 | 18 | The project maintainers use LGTM (Looks Good To Me) in comments on the pull 19 | request to indicate acceptance prior to merging. A change requires LGTMs from 20 | two project maintainers. If the code is written by a maintainer, the change 21 | only requires one additional LGTM. 22 | 23 | ## Reviewing Pull Requests 24 | 25 | We recommend reviewing pull requests directly within GitHub. This allows a 26 | public commentary on changes, providing transparency for all users. When 27 | providing feedback be civil, courteous, and kind. Disagreement is fine, so long 28 | as the discourse is carried out politely. If we see a record of uncivil or 29 | abusive comments, we will revoke your commit privileges and invite you to leave 30 | the project. 31 | 32 | During your review, consider the following points: 33 | 34 | ### Does the change have positive impact? 35 | 36 | Some proposed changes may not represent a positive impact to the project. Ask 37 | whether or not the change will make understanding the code easier, or if it 38 | could simply be a personal preference on the part of the author (see 39 | [bikeshedding](https://en.wiktionary.org/wiki/bikeshedding)). 40 | 41 | Pull requests that do not have a clear positive impact should be closed without 42 | merging. 43 | 44 | ### Do the changes make sense? 45 | 46 | If you do not understand what the changes are or what they accomplish, ask the 47 | author for clarification. Ask the author to add comments and/or clarify test 48 | case names to make the intentions clear. 49 | 50 | At times, such clarification will reveal that the author may not be using the 51 | code correctly, or is unaware of features that accommodate their needs. If you 52 | feel this is the case, work up a code sample that would address the pull 53 | request for them, and feel free to close the pull request once they confirm. 54 | 55 | ### Does the change introduce a new feature? 56 | 57 | For any given pull request, ask yourself "is this a new feature?" If so, does 58 | the pull request (or associated issue) contain narrative indicating the need 59 | for the feature? If not, ask them to provide that information. 60 | 61 | Are new unit tests in place that test all new behaviors introduced? If not, do 62 | not merge the feature until they are! Is documentation in place for the new 63 | feature? (See the documentation guidelines). If not do not merge the feature 64 | until it is! Is the feature necessary for general use cases? Try and keep the 65 | scope of any given component narrow. If a proposed feature does not fit that 66 | scope, recommend to the user that they maintain the feature on their own, and 67 | close the request. You may also recommend that they see if the feature gains 68 | traction among other users, and suggest they re-submit when they can show such 69 | support. 70 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # WARNING: This repository is no longer maintained :warning: 2 | 3 | > This repository will not be updated. The repository will be kept available in read-only mode. 4 | 5 | # Change Point Detection in Time Series Sensor data 6 | 7 | > Data Science Experience is now Watson Studio. Although some images in this code pattern may show the service as Data Science Experience, the steps and processes will still work. 8 | 9 | ## Overview and Goal 10 | 11 | This code pattern is intended for any developer who wants to experiment, learn, enhance and implement a new method for Statistically detecting Change point in Sensor data. Sensors mounted on devices like IoT devices, Automated manufacturing like Robot arms, Process monitoring and Control equipment etc., collect and transmit data on a continuous basis which is Time stamped. 12 | 13 | This code pattern takes you through end to end flow of steps in collating statistics on such Time series data and identify if a Change point has occurred. Core building blocks would include computing Statistical parameters from the Time series data, which compares a Previous dataset of a certain Time range in the past with the Current Series in a recent Time range. Statistical comparison between these two results in detection of any change points. R statistical software is used in this pattern with sample Sensor data loaded into the Data Science experience cloud. 14 | 15 | All the intermediary steps are modularized and all code open sourced to enable developers to use / modify the modules / sub-modules as they see fit for their specific application. 16 | 17 | This code pattern utilizes IoT sensor data and its primary goal is to statistically identify the change point in this sensor data rather than the acquisition and storage of the data itself. For sake of completeness of the flow, a simulation of the IoT data acquisition is included as a first step. 18 | 19 | ![png](doc/source/images/cpd_arch_flow.png) 20 | 21 | A detailed pattern of acquisition and storage of IoT sensor data is already covered extensively elsewhere. References to the details of these patterns are also given. 22 | 23 | When you have completed this code pattern, you will understand how to 24 | 25 | * Write data from a IoT source to a database 26 | * Create and Run a Jupyter Notebook in Watson Studio 27 | * Run R statistical software code in Jupyter Notebook 28 | * Read data from database in R notebook and statistically analyze the data 29 | * Generate results in the form of visualisation plots 30 | * Execute R statistical functions to detect Change point in data 31 | * Output and save results in Jupyter Notebook 32 | 33 | This code pattern can be logically split into 2 major parts: 34 | 35 | * Data acquisition and storage of IoT Sensor data using Node Red flows and DB2: The Steps 1 to 4 below cover the details of this part of the pattern. 36 | * Data retrieval and statistical analysis using R - Jupyter notebooks to analyze and detect change points in the data: The Steps 5 to 8 below cover the details of this part of the pattern. 37 | 38 | ## Prerequisites 39 | 40 | You will need the following accounts and tools: 41 | 42 | * [IBM Cloud account](https://cloud.ibm.com/registration/) 43 | * [Bluemix CLI](https://cloud.ibm.com/docs/cli/reference/bluemix_cli/index.html#getting-started) 44 | 45 | ## Steps: 46 | 47 | 1. Log into IBM Cloud and create IBM Cloud services 48 | 2. Create Node-RED Application to load IoT data into DB2 table by using the provided [.json configuration file](configuration/node-red.json) 49 | 3. Read IoT data from the sample csv file provided. The Node-RED flow can be changed to read from IoT devices directly 50 | 4. Import the [sample data](data/sample_sensordata2016_1s3dys.csv) into a DB2 table using the Node-RED flow 51 | 5. User configures the parameters in [.json dsx configuration file](configuration/cpd_dsx_config.json) that will be used in Data Science experience and updates credentials to read the configuration file 52 | 6. In R notebook flow, user then updates credentials to read relevant Sensor data subset from the DB2 table. Data from the cloud database will be read by R Spark dataframe in Watson Studio notebook. The user will further extract the 2 series of datasets to be compared. R notebook will use open R libraries and Custom built function components to get the statistics computed. User will generate visual comparison charts to get visual insights on changes in behavior of the sensor values. These Statistical metrics will be compared and the changes analyzed using the Custom functions written in R 53 | 7. In Data science experience R runs on Spark engine to ensure scalability and performance 54 | 8. Object storage is used to store the configuration file where Watson Studio reads the parameters from. The results can also be stored in Object storage if needed 55 | 56 | Developer can reuse all components that support the above steps like 57 | * Reading specific Time series data points from database like Time stamp, Sensor ID, Sensor values 58 | * Time stamp conversion functions, filtering data for specific sensor 59 | * User configurable Time ranges for Splitting Time series data into 2 sets for comparison 60 | * Computations of key statistics that statistically compress these series for useful comparison 61 | * Build a dictionary of statistics to consistently compare these data sets 62 | * Based on threshold specified by User, detect if a Change point has occurred in the data 63 | 64 | ## Included components 65 | 66 | * [IBM Node-RED Cloud Foundry App](https://cloud.ibm.com/catalog/starters/node-red-starter): Develop, deploy, and scale server-side JavaScript® apps with ease. The IBM SDK for Node.js™ provides enhanced performance, security, and serviceability. 67 | 68 | * [IBM Watson Studio](https://www.ibm.com/cloud/watson-studio): Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark. 69 | 70 | * [DB2 Warehouse on Cloud](https://cloud.ibm.com/catalog/services/db2-warehouse-on-cloud): IBM Db2 Warehouse on Cloud is a fully-managed, enterprise-class, cloud data warehouse service. Powered by IBM BLU Acceleration. 71 | 72 | * [IBM Cloud Object Storage](https://cloud.ibm.com/catalog/services/cloud-object-storage?cm_sp=dw-bluemix-_-code-_-devcenter): An IBM Cloud service that provides an unstructured cloud data store to build and deliver cost effective apps and services with high reliability and fast speed to market. 73 | 74 | * [Internet of Things Platform](https://cloud.ibm.com/catalog/services/internet-of-things-platform): This service is the hub for IBM Watson IoT and lets you communicate with and consume data from connected devices and gateways. Use the built-in web console dashboards to monitor your IoT data and analyze it in real time. 75 | 76 | ## Featured technologies 77 | 78 | * [Jupyter Notebooks](https://jupyter.org/): An open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. 79 | 80 | # Watch the Video 81 | 82 | [![](https://img.youtube.com/vi/SKRYaz7nMEs/0.jpg)](https://youtu.be/DFY7wcwRZAE) 83 | 84 | # Steps 85 | 86 | Follow these steps to setup and run this developer pattern. The steps are 87 | described in detail below. 88 | 89 | 1. [Sign up for the Watson Studio](#1-sign-up-for-the-data-science-experience) 90 | 1. [Create IBM Cloud services](#2-create-bluemix-services) 91 | 1. [Create Node-RED App and inject IoT data](#3-create-node-red-app-and-inject-iot-data) 92 | 1. [Create the notebook](#4-create-the-r-spark-jupyter-notebook) 93 | 1. [Add the data and configuraton file](#5-add-the-configuration-and-data-access-details) 94 | 1. [Run the notebook](#6-run-the-notebook) 95 | 1. [Download the results](#7-view-the-results) 96 | 97 | ## 1. Sign up for the Watson Studio 98 | 99 | Sign up for IBM's [Watson Studio](https://www.ibm.com/cloud/watson-studio). By signing up for the Watson Studio, two services: ``Apache Spark`` and ``Object Storage`` will be created in your IBM Cloud account. 100 | 101 | ## 2. Create IBM Cloud services 102 | 103 | Create the IBM Cloud services by following the links below. 104 | * [**Object Storage in IBM Cloud**](https://cloud.ibm.com/catalog/services/cloud-object-storage) 105 | 106 | 1. Choose an appropriate name for the service `Service Name` and choose `Free` Pricing Plan. Click on `Create`. 107 | ![png](doc/source/images/cpd_bmx_addObjectStorage.png) 108 |
109 | 2. Click on Object Storage service instance on IBM Cloud Dashboard. Choose the `region` and create a Container unit using `Add a container` link. 110 | ![png](doc/source/images/cpd_bmx_objstorage_createContainer.png) 111 |
112 | 3. Upload the [sample data file](data/sample_sensordata2016_1s3dys.csv) in Object storage Container. 113 | 114 | 115 | * [**DB2 Warehouse on Cloud**](https://cloud.ibm.com/catalog/services/db2-warehouse-on-cloud) 116 | 117 | ![](doc/source/images/cpd_db2whs_onbluemix.png) 118 | 119 | 1. Once service is created, click on `DB2 Warehouse on Cloud` service instance on IBM Cloud Dashboard. Click `Open` to launch the Dashboard. 120 | 2. Click on `Explore` from the panel, choose schema and then create a `New Table`. 121 | ![png](doc/source/images/cpd_bmx_db2ws_createTable.png) 122 | 3. Create a new database table `CHANGEPOINTIOT` with following schema: 123 | { 124 | SENSORID VARCHAR(20) 125 | TIMESTAMP VARCHAR(100) 126 | SENSORVALUE DECIMAL(8,5) 127 | SENSORUNITS VARCHAR(100) 128 | } 129 | 130 | 131 | * [**Internet of Things Platform**](https://cloud.ibm.com/catalog/services/internet-of-things-platform) 132 | 133 | ![](doc/source/images/cpd_bmx_addIOT.png) 134 | 135 | 1. Once service is created, click on `Internet of Things Platform` service instance on IBM Cloud Dashboard. Launch the Watson IoT Platform Dashboard. 136 | 2. Register a device on Watson IoT Platform, refer to step 3 and step 4 in https://developer.ibm.com/recipes/tutorials/how-to-register-devices-in-ibm-iot-foundation/ 137 | 3. Generate API Keys to establish a connection to IoT Platform from your application. Refer step 5 in https://developer.ibm.com/recipes/tutorials/how-to-register-devices-in-ibm-iot-foundation/ to generate API Keys. Make a note of the generated keys, it will be used later. 138 | 139 | 140 | * [**Watson Studio**](https://cloud.ibm.com/catalog/services/data-science-experience) 141 | 142 | ![](doc/source/images/cpd_dsx_menu.png.png) 143 | ![](doc/source/images/cpd_dsx_createservice.png) 144 | 145 | 146 | ## 3. Create Node-RED App and inject IoT data 147 | 148 | Create the Node-RED Starter application by following the link. Choose an appropriate name for the Node-RED application - `App name:`. Click on `Create`. 149 | 150 | [**Node-RED Starter**](https://cloud.ibm.com/catalog/starters/node-red-starter) 151 | 152 | ![png](doc/source/images/cpd_bmx_nodered_create.png) 153 | 154 | * On the newly created Node-RED application page, Click on `Visit App URL` to launch the Node-RED editor once the application is in `Running` state. 155 | * On the `Welcome to your new Node-RED instance on IBM Cloud` screen, Click on `Next` 156 | * On the `Secure your Node-RED editor` screen, enter a username and password to secure the Node-RED editor and click on `Next` 157 | * On the `Browse available IBM Cloud nodes` screen, click on `Next` 158 | * On the `Finish the install` screen, click on Finish 159 | * Click on `Go to your Node-RED flow editor` 160 |  * Install the following nodes before importing the flow. To do this select ‘Manage Palette’ from the menu (top right), and then select the install tab in the palette. Search for new nodes name to install and click on `Install`. 161 | 162 | - `node-red-contrib-objectstore` 163 | 164 | ![png](doc/source/images/cpd_bmx_nodered_addNewNode.png) 165 |
166 | - `node-red-contrib-ibm-watson-iot` 167 | 168 | ![png](doc/source/images/cpd_bmx_nodered_addNewNode1.png) 169 | 170 | #### Import Node-RED flow by importing the [configuration .json](configuration/node-red.json) 171 | The flow json for Node-RED can be found under `configuration` directory. 172 | * Download the `configuration/node-red.json` 173 | * Open the file with a text editor and copy the contents to Clipboard 174 | * On the Node-RED flow editor, click the Menu and select `Import` -> `Clipboard` and paste the contents 175 | 176 | ![png](doc/source/images/cpd_bmx_import_nodered_flow.png) 177 | 178 | #### Adjustments to the node properties in Node-RED Flow 179 | 1. Object Storage node (getFileData_in_buffer): Provide your Object Storage service credentials. Service credentials are available in IBM Cloud service instance. Configure node in buffer mode to read the file from your object storage service. Ensure the sample data is loaded into Object storage as explained in Create IBM Cloud Services section above. 180 | 181 | 2. Watson-IoT node (TemperatureSensor): Configure this with a registered device on Watson IoT Platform. To configure Watson IoT node in node-red, refer to : https://developer.ibm.com/recipes/tutorials/simulating-a-device-and-publishing-034messages034-to-ibm-iot-platform-from-a-nodered-034watson-iot034-platform-node/ 182 | 183 | 3. IBM IoT node: Configure IBM IoT node to receive events from Watson IoT Platform using the API keys generated in Create IBM Cloud Services section. To setup IBM IoT Node in node-red refer to step 5 in https://developer.ibm.com/recipes/tutorials/getting-started-with-watson-iot-platform-using-node-red/ 184 | 185 | 4. dashDB node (CHANGEPOINTIOT): Use credentials of DB2 Warehouse on Cloud service. Service credentials are available in IBM Cloud service instance. Provide database table name `CHANGEPOINTIOT` in which sensor data will get populated. 186 | 187 | #### Deploy the Node-RED flow by clicking on the `Deploy` button 188 | 189 | ![](doc/source/images/cpd_bmx_nodered_deploy.png) 190 | 191 | Node-red flow is designed as: 192 | 1. The csv file with sample sensor data is uploaded in object storage. 193 | 2. Prepare a csv string from the sample data file and give this string, as an input to csv node. 194 | 3. csv node will act as a device simulator and it will trigger an event of temperature sensor for each row of data. 195 | 4. The events sent by temperature sensor will be received by IBM IoT Platform. 196 | 5. This data will be prepared and then stored in the database. 197 | 6. Data from DB can be used in R Jupyter notebook for analytics. 198 | 199 | 200 | #### Inject the data in Node-RED Flow 201 | In Node-RED Flow, click on the input of `inject` node. It will trigger the execution of the node-red flow and on successful execution, data will get stored to DB2 table `CHANGEPOINTIOT`. 202 | 203 | ## 4. Create the R Spark Jupyter notebook 204 | 205 | Use the menu on the left to select `My Projects` and then `Default Project`. 206 | Click on `Add notebooks` (upper right) to create a notebook. 207 | * Select the `From URL` tab. 208 | * Enter a name for the notebook. 209 | * Optionally, enter a description for the notebook. 210 | * Enter this Notebook URL: 211 | https://github.com/IBM/detect-timeseriesdata-change/blob/master/notebooks/watson_changepoint_detection.ipynb 212 | * Click the `Create Notebook` button. 213 | * Upload the sample .json Watson Studio configuration file to Object storage from URL: 214 | https://github.com/IBM/detect-timeseriesdata-change/blob/master/configuration/cpd_dsx_config.json 215 | 216 | ## 5. Add the configuration and data access details 217 | 218 | #### Fix-up configuration parameter .json file name and values 219 | 220 | Once the files have been uploaded into ``Object Storage`` you need to update the variables that refer to the .json configuration files in the R - Jupyter Notebook. 221 | 222 | In the notebook, update the Watson Studio configuration .json file name in section 2.1.1 223 | ![png](doc/source/images/cpd_dsxconfig_setfilename.png) 224 | 225 | Edit the [Watson Studio configuration .json file](configuration/cpd_dsx_config.json) 226 | Update the `paramvalue` ONLY to suit your requirements and save the .json file 227 | Retain the rest of the format and composition of the .json file 228 | 229 | ![png](doc/source/images/cpd_dsxconfig_json.png) 230 | 231 | The descriptions of the parameters that can be configured are as below. 232 | 1. coltimestamp: Name of the column which holds the Time stamp of data 233 | recorded by Sensor 234 | 2. colsensorid: Name of the column which holds the Sensor identification 235 | 3. colsensorvalue: Name of the column that stores the values measured by sensor 236 | 4. sensorid: Sensor ID for which the analysis needs to be applied 237 | 5. datatimeformat: Time format of the data in the data frame 238 | 6. intimezone: Time zone for the Time stamps 239 | 7. rangetimeformat: Time format which is used for specifying the time ranges 240 | 8. Pfrom: Start Time for first series Time range 241 | 9. Pto: End Time for first series Time range 242 | 10. Cfrom: Start Time for second series Time range 243 | 11. Cto: End Time for second series Time range 244 | 12. thresholdpercent: Set the threshold percentage of change if detected 245 | 246 | 247 | * In section 2.1.2 of Watson Studio notebook, Insert (replace) your own Object storage 248 | file credentials to read the .json configuration file 249 | * Also replace the function name in the block that Read json configuration file 250 | in section 2.1.3 251 | 252 | ![png](doc/source/images/cpd_dsxjson_creds.png) 253 | ![png](doc/source/images/cpd_dsxconfig_jsonread.png) 254 | 255 | 256 | 257 | #### Add the data and configuration to the notebook 258 | Use `Find and Add Data` (look for the `10/01` icon) 259 | and its `Connectsions` tab. You must be able to see your database connection created earlier. 260 | From there you can click `Insert to Code` under the `Data connection` list and add `ibm DBR code` 261 | with connection credentials to the flow. 262 | 263 | ![png](doc/source/images/cpd_connreaddata_fromdashdb.png) 264 | 265 | > Note: If you don't have your own data and configuration files, you can reuse our example in the "Read IoT Sensor data from database" 266 | section. Look in the `data/sensordata2016_1s3dys.csv` directory for data file. 267 | 268 | ![](doc/source/images/cpd_readdata_fromdashdb.png) 269 | 270 | 271 | ## 6. Run the notebook 272 | 273 | When a notebook is executed, what is actually happening is that each code cell in 274 | the notebook is executed, in order, from top to bottom. 275 | 276 | Each code cell is selectable and is preceded by a tag in the left margin. The tag 277 | format is `In [x]:`. Depending on the state of the notebook, the `x` can be: 278 | 279 | * A blank, this indicates that the cell has never been executed. 280 | * A number, this number represents the relative order this code step was executed. 281 | * A `*`, this indicates that the cell is currently executing. 282 | 283 | There are several ways to execute the code cells in your notebook: 284 | 285 | * One cell at a time. 286 | * Select the cell, and then press the `Play` button in the toolbar. 287 | * Batch mode, in sequential order. 288 | * From the `Cell` menu bar, there are several options available. For example, you 289 | can `Run All` cells in your notebook, or you can `Run All Below`, that will 290 | start executing from the first cell under the currently selected cell, and then 291 | continue executing all cells that follow. 292 | * At a scheduled time. 293 | * Press the `Schedule` button located in the top right section of your notebook 294 | panel. Here you can schedule your notebook to be executed once at some future 295 | time, or repeatedly at your specified interval. 296 | 297 | ## 7. View the results 298 | 299 | The notebook outputs the results in the Notebook which can be copied to clipboard 300 | 301 | ![](doc/source/images/cpd_line_plot.png) 302 | ![](doc/source/images/cpd_box_plot.png) 303 | ![](doc/source/images/cpd_notebook_results.png) 304 | 305 | 306 | The graphs give a visual indication of how the Sensor values behave during the 2 time periods 307 | 308 | Statistics on these 2 time periods like averages, standard deviations, quartiles are computed and deviations computed for each of them. Then a overall deviation is computed and compared against the threshold set earlier in the Watson Studio configuration file 309 | 310 | Based on the threshold deviation specified by the user, if the overall computed deviation exceeds the threshold configured, custom R functions will output if there is a Change point occurrence detected 311 | 312 | 313 | # Troubleshooting 314 | 315 | [See DEBUGGING.md](DEBUGGING.md) 316 | 317 | ## Useful links 318 | 319 | * [IBM Cloud](https://console.bluemix.net) 320 | * [IBM Cloud Documentation](https://console.bluemix.net/docs/) 321 | * [IBM Cloud Developers Community](https://developer.ibm.com/bluemix/) 322 | * [IBM Watson Internet of Things](https://www.ibm.com/internet-of-things) 323 | * [IBM Watson IoT Platform](https://www.ibm.com/internet-of-things/solutions) 324 | * [IBM Watson IoT Platform Developers Community](https://developer.ibm.com/iotplatform/) 325 | * [Simulate IoT Device](https://github.com/IBM/manage-control-device-node-red) 326 | * [Node-RED](https://nodered.org/) 327 | 328 | 329 | 330 | # License 331 | 332 | This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the [Developer Certificate of Origin, Version 1.1 (DCO)](https://developercertificate.org/) and the [Apache Software License, Version 2](https://www.apache.org/licenses/LICENSE-2.0.txt). 333 | 334 | [Apache Software License (ASL) FAQ](https://www.apache.org/foundation/license-faq.html#WhatDoesItMEAN) 335 | -------------------------------------------------------------------------------- /configuration/cpd_dsx_config.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "paramname": "coltimestamp", 4 | "paramvalue": "TIMESTAMP" 5 | }, 6 | { 7 | "paramname": "colsensorid", 8 | "paramvalue": "SENSORID" 9 | }, 10 | { 11 | "paramname": "colsensorvalue", 12 | "paramvalue": "SENSORVALUE" 13 | }, 14 | { 15 | "paramname": "sensorid", 16 | "paramvalue": "3B1" 17 | }, 18 | { 19 | "paramname": "datatimeformat", 20 | "paramvalue": "%d-%m-%Y %H:%M:%S" 21 | }, 22 | { 23 | "paramname": "intimezone", 24 | "paramvalue": "GMT" 25 | }, 26 | { 27 | "paramname": "rangetimeformat", 28 | "paramvalue": "%Y%m%d %H:%M:%S" 29 | }, 30 | { 31 | "paramname": "Pfrom", 32 | "paramvalue": "20160324 00:00:00" 33 | }, 34 | { 35 | "paramname": "Pto", 36 | "paramvalue": "20160325 00:00:00" 37 | }, 38 | { 39 | "paramname": "Cfrom", 40 | "paramvalue": "20160325 00:00:00" 41 | }, 42 | { 43 | "paramname": "Cto", 44 | "paramvalue": "20160326 00:00:00" 45 | }, 46 | { 47 | "paramname": "thresholdpercent", 48 | "paramvalue": 25 49 | } 50 | ] 51 | -------------------------------------------------------------------------------- /configuration/node-red.json: -------------------------------------------------------------------------------- 1 | [{"id":"21ba2a19.d191ee","type":"inject","z":"a9acee2f.4591e","name":"inject","topic":"","payload":"","payloadType":"date","repeat":"","crontab":"","once":false,"x":103,"y":47,"wires":[["add24afb.c112c"]]},{"id":"add24afb.c112c","type":"os-get","z":"a9acee2f.4591e","name":"getFileData_in_buffer","container":"devjourney1","objectname":"sample_sensordata2016_1s3dys.csv","filepath":"/","filename":"data.csv","mode":"1","osconfig":"b98d0811.d91c6","x":318,"y":48.25,"wires":[["395df279.afa976"]]},{"id":"395df279.afa976","type":"function","z":"a9acee2f.4591e","name":"buffer2string","func":"var chunk = msg.payload;\nnode.log(\"File Content: \", chunk);\nvar textChunk = chunk.toString('utf8');\nnode.log(\"File Content as String: \", textChunk);\n\nvar lines = \"\";\nlines = textChunk.split(\"\\n\");\nnode.log(\"records length:\" , lines.length);\n\nmsg.payload = textChunk;\nreturn msg;","outputs":1,"noerr":0,"x":255,"y":137,"wires":[["8dc81b2f.2da0f"]]},{"id":"d9272fe0.e2c1c8","type":"ibmiot in","z":"a9acee2f.4591e","authentication":"apiKey","apiKey":"313a7456.b562d4","inputType":"evt","deviceId":"3B1","applicationId":"","deviceType":"+TemperatureSensor","eventType":"+","commandType":"","format":"json","name":"IBM IoT","service":"registered","allDevices":true,"allApplications":"","allDeviceTypes":true,"allEvents":true,"allCommands":"","allFormats":true,"qos":0,"x":97.5,"y":247.5,"wires":[["57db51be.768c3"]]},{"id":"8dc81b2f.2da0f","type":"csv","z":"a9acee2f.4591e","name":"","sep":",","hdrin":"","hdrout":"","multi":"one","ret":"\\n","temp":"\"SensorID\",\"TimeStamp\",\"SensorValue\",\"SensorUnits\"","x":456.5,"y":143,"wires":[["a5c79883.2a1078"]]},{"id":"a5c79883.2a1078","type":"wiotp out","z":"a9acee2f.4591e","authType":"d","qs":"false","qsDeviceId":"","deviceKey":"3debc92b.b7dca6","deviceType":"","deviceId":"","event":"event","format":"json","name":"TemperatureSensor","x":663.5,"y":194.75,"wires":[]},{"id":"8463f370.c7d898","type":"dashDB out","z":"a9acee2f.4591e","dashDB":"","service":"Db2 Warehouse on Cloud-pv","table":"CHANGEPOINTIOT","name":"","x":484.5,"y":353.5,"wires":[]},{"id":"57db51be.768c3","type":"function","z":"a9acee2f.4591e","name":"IoT Data","func":"var text = msg;\n\nvar output = {};\noutput.payload =\n{\n SENSORID : text.payload.d.SensorID,\n TIMESTAMP : text.payload.d.TimeStamp,\n SENSORVALUE : text.payload.d.SensorValue,\n SENSORUNITS : text.payload.d.SensorUnits\n}\nreturn output;","outputs":"1","noerr":0,"x":264.5,"y":302,"wires":[["8463f370.c7d898"]]},{"id":"b98d0811.d91c6","type":"os-config","z":"","cfgtype":"api","region":"dallas","projectId":"683b6ba01c904c529487c93a6947adb4","userId":"e8d0a26016124513a768d4974813fb42","userName":"admin_dd2cba4f9d2aaa9b3c032897fb2baefc7fc7e3c8","password":"K9oTd-s}R-6o9*Zm","name":"ObjStore"},{"id":"313a7456.b562d4","type":"ibmiot","z":"54f53ee2.c5c118","name":"","keepalive":"180","serverName":"","cleansession":true,"appId":"","shared":false},{"id":"3debc92b.b7dca6","type":"wiotp-credentials","z":"","name":"TemperatureSensor","org":"sdlm5w","devType":"TemperatureSensor","devId":"3B1"}] 2 | -------------------------------------------------------------------------------- /data/sample_sensordata2016_1s3dys.csv: -------------------------------------------------------------------------------- 1 | "SensorID","TimeStamp","SensorValue","SensorUnits" 2 | "3B1","24-03-2016 00:00:00",9.225,"FAHRENHEIT" 3 | "3B1","24-03-2016 00:10:00",8.61,"FAHRENHEIT" 4 | "3B1","24-03-2016 00:20:00",8.61,"FAHRENHEIT" 5 | "3B1","24-03-2016 00:30:00",8.61,"FAHRENHEIT" 6 | "3B1","24-03-2016 00:40:00",8.61,"FAHRENHEIT" 7 | "3B1","24-03-2016 00:50:00",8.61,"FAHRENHEIT" 8 | "3B1","24-03-2016 01:00:00",8.61,"FAHRENHEIT" 9 | "3B1","24-03-2016 01:10:00",8.61,"FAHRENHEIT" 10 | "3B1","24-03-2016 01:20:00",8.61,"FAHRENHEIT" 11 | "3B1","24-03-2016 01:30:00",8.61,"FAHRENHEIT" 12 | "3B1","24-03-2016 01:40:00",8.61,"FAHRENHEIT" 13 | "3B1","24-03-2016 01:50:00",8.61,"FAHRENHEIT" 14 | "3B1","24-03-2016 02:00:00",8.61,"FAHRENHEIT" 15 | "3B1","24-03-2016 02:10:00",8.61,"FAHRENHEIT" 16 | "3B1","24-03-2016 02:20:00",8.61,"FAHRENHEIT" 17 | "3B1","24-03-2016 02:30:00",8.61,"FAHRENHEIT" 18 | "3B1","24-03-2016 02:40:00",8.61,"FAHRENHEIT" 19 | "3B1","24-03-2016 02:50:00",8.61,"FAHRENHEIT" 20 | "3B1","24-03-2016 03:00:00",8.61,"FAHRENHEIT" 21 | "3B1","24-03-2016 03:10:00",8.61,"FAHRENHEIT" 22 | "3B1","24-03-2016 03:20:00",8.61,"FAHRENHEIT" 23 | "3B1","24-03-2016 03:30:00",8.61,"FAHRENHEIT" 24 | "3B1","24-03-2016 03:40:00",8.61,"FAHRENHEIT" 25 | "3B1","24-03-2016 03:50:00",8.61,"FAHRENHEIT" 26 | "3B1","24-03-2016 04:00:00",8.61,"FAHRENHEIT" 27 | "3B1","24-03-2016 04:10:00",8.61,"FAHRENHEIT" 28 | "3B1","24-03-2016 04:20:00",8.61,"FAHRENHEIT" 29 | "3B1","24-03-2016 04:30:00",8.61,"FAHRENHEIT" 30 | "3B1","24-03-2016 04:40:00",8.61,"FAHRENHEIT" 31 | "3B1","24-03-2016 04:50:00",8.61,"FAHRENHEIT" 32 | "3B1","24-03-2016 05:00:00",8.61,"FAHRENHEIT" 33 | "3B1","24-03-2016 05:10:00",8.61,"FAHRENHEIT" 34 | "3B1","24-03-2016 05:20:00",8.61,"FAHRENHEIT" 35 | "3B1","24-03-2016 05:30:00",8.61,"FAHRENHEIT" 36 | "3B1","24-03-2016 05:40:00",8.61,"FAHRENHEIT" 37 | "3B1","24-03-2016 05:50:00",8.61,"FAHRENHEIT" 38 | "3B1","24-03-2016 06:00:00",8.61,"FAHRENHEIT" 39 | "3B1","24-03-2016 06:10:00",8.61,"FAHRENHEIT" 40 | "3B1","24-03-2016 06:20:00",27.675,"FAHRENHEIT" 41 | "3B1","24-03-2016 06:30:00",22.14,"FAHRENHEIT" 42 | "3B1","24-03-2016 06:40:00",15.99,"FAHRENHEIT" 43 | "3B1","24-03-2016 06:50:00",13.53,"FAHRENHEIT" 44 | "3B1","24-03-2016 07:00:00",11.685,"FAHRENHEIT" 45 | "3B1","24-03-2016 07:10:00",11.07,"FAHRENHEIT" 46 | "3B1","24-03-2016 07:20:00",10.455,"FAHRENHEIT" 47 | "3B1","24-03-2016 07:30:00",10.455,"FAHRENHEIT" 48 | "3B1","24-03-2016 07:40:00",9.84,"FAHRENHEIT" 49 | "3B1","24-03-2016 07:50:00",9.84,"FAHRENHEIT" 50 | "3B1","24-03-2016 08:00:00",9.84,"FAHRENHEIT" 51 | "3B1","24-03-2016 08:10:00",9.225,"FAHRENHEIT" 52 | "3B1","24-03-2016 08:20:00",9.225,"FAHRENHEIT" 53 | "3B1","24-03-2016 08:30:00",9.225,"FAHRENHEIT" 54 | "3B1","24-03-2016 08:40:00",9.225,"FAHRENHEIT" 55 | "3B1","24-03-2016 08:50:00",9.84,"FAHRENHEIT" 56 | "3B1","24-03-2016 09:00:00",9.225,"FAHRENHEIT" 57 | "3B1","24-03-2016 09:10:00",9.225,"FAHRENHEIT" 58 | "3B1","24-03-2016 09:20:00",9.225,"FAHRENHEIT" 59 | "3B1","24-03-2016 09:30:00",9.225,"FAHRENHEIT" 60 | "3B1","24-03-2016 09:40:00",8.61,"FAHRENHEIT" 61 | "3B1","24-03-2016 09:50:00",9.225,"FAHRENHEIT" 62 | "3B1","24-03-2016 10:00:00",9.225,"FAHRENHEIT" 63 | "3B1","24-03-2016 10:10:00",9.225,"FAHRENHEIT" 64 | "3B1","24-03-2016 10:20:00",9.225,"FAHRENHEIT" 65 | "3B1","24-03-2016 10:30:00",9.225,"FAHRENHEIT" 66 | "3B1","24-03-2016 10:40:00",9.225,"FAHRENHEIT" 67 | "3B1","24-03-2016 10:50:00",9.84,"FAHRENHEIT" 68 | "3B1","24-03-2016 11:00:00",9.84,"FAHRENHEIT" 69 | "3B1","24-03-2016 11:10:00",9.84,"FAHRENHEIT" 70 | "3B1","24-03-2016 11:20:00",9.84,"FAHRENHEIT" 71 | "3B1","24-03-2016 11:30:00",14.76,"FAHRENHEIT" 72 | "3B1","24-03-2016 11:40:00",11.07,"FAHRENHEIT" 73 | "3B1","24-03-2016 11:50:00",10.455,"FAHRENHEIT" 74 | "3B1","24-03-2016 12:00:00",10.455,"FAHRENHEIT" 75 | "3B1","24-03-2016 12:10:00",9.84,"FAHRENHEIT" 76 | "3B1","24-03-2016 12:20:00",9.84,"FAHRENHEIT" 77 | "3B1","24-03-2016 12:30:00",9.84,"FAHRENHEIT" 78 | "3B1","24-03-2016 12:40:00",9.84,"FAHRENHEIT" 79 | "3B1","24-03-2016 12:50:00",15.99,"FAHRENHEIT" 80 | "3B1","24-03-2016 13:00:00",28.905,"FAHRENHEIT" 81 | "3B1","24-03-2016 13:10:00",43.665,"FAHRENHEIT" 82 | "3B1","24-03-2016 13:20:00",49.815,"FAHRENHEIT" 83 | "3B1","24-03-2016 13:30:00",52.89,"FAHRENHEIT" 84 | "3B1","24-03-2016 13:40:00",52.275,"FAHRENHEIT" 85 | "3B1","24-03-2016 13:50:00",52.89,"FAHRENHEIT" 86 | "3B1","24-03-2016 14:00:00",34.44,"FAHRENHEIT" 87 | "3B1","24-03-2016 14:10:00",25.215,"FAHRENHEIT" 88 | "3B1","24-03-2016 14:20:00",28.29,"FAHRENHEIT" 89 | "3B1","24-03-2016 14:30:00",28.29,"FAHRENHEIT" 90 | "3B1","24-03-2016 14:40:00",19.065,"FAHRENHEIT" 91 | "3B1","24-03-2016 14:50:00",16.605,"FAHRENHEIT" 92 | "3B1","24-03-2016 15:00:00",15.99,"FAHRENHEIT" 93 | "3B1","24-03-2016 15:10:00",16.605,"FAHRENHEIT" 94 | "3B1","24-03-2016 15:20:00",14.76,"FAHRENHEIT" 95 | "3B1","24-03-2016 15:30:00",14.145,"FAHRENHEIT" 96 | "3B1","24-03-2016 15:40:00",33.21,"FAHRENHEIT" 97 | "3B1","24-03-2016 15:50:00",20.295,"FAHRENHEIT" 98 | "3B1","24-03-2016 16:00:00",16.605,"FAHRENHEIT" 99 | "3B1","24-03-2016 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