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This video continues Module 1: ThingWorx Analytics Overview of the ThingWorx Analytics Training videos. It covers some of the functionality of the ThingWorx platform, as well as ThingWorx Analytics capabilities.

This video begins Module 1: ThingWorx Analytics Overview of the ThingWorx Analytics Training videos. It covers some of the functionality of the ThingWorx platform, as well as ThingWorx Analytics capabilities.

  Create an Engine Failure Prediction GUI to warn about customer issues.   GUIDE CONCEPT   This guide will use ThingWorx Foundation's Mashup Builder to create a Graphical User Interface (GUI) to display results from Analytics Manager comparisons of your analytical model to real-time data.   Following the steps in this guide, you will learn how to utilize Widgets and backend data to quickly visualize customer failure conditions.     YOU'LL LEARN HOW TO   Create a Mashup Add Widgets to represent different data Bring Backend Data into the Mashup Tie data to Widgets View Analytical Results in a convenient GUI   NOTE:  The estimated time to complete all parts of this guide is 30 minutes.     Step 1: Scenario   In this guide, we're finishing up with the MotorCo scenario where an engine can fail catastrophically in a low-grease condition.   In previous guides, you've gathered and exported engine vibration-data from an Edge MicroServer (EMS) and used it to build an engine analytics model. You've even put that analytical model into service to give near-immediate results from current engine-vibration readings.   The goal of this guide is to create a GUI to visualize those predicted "low grease" conditions to facilitate customer warnings.     GUI-creation to visualize analytical model deployment can be extremely helpful for the automative segment in particular. For instance, each car that comes off the factory line could have an EMS constantly sending data from which an analytical model could automatically detect engine trouble.   This could enable your company to offer an engine monitoring subscription service to your customers.   This guide will show you how to visualize the results of an engine analytic model for a "smart, connected products" play.     Step 2: Create Mashup   Mashups are ThingWorx Foundation's method of creating Graphical User Interfaces (GUIs).   Mashups are created through the Mashup Builder interface.   Before you can use this drag-and-drop interface, you must first create a new Mashup.      1. In ThingWorx Foundation Composer, click Browse > Visualization > Mashups.      2. Click + New.        3. Leave the defaults and click OK.        4. In the Name field, type EFPG_Mashup.      5. If Project is not already set, search for and select PTCDefaultProject.        6. At the top, click Save.        7. At the top, click Design. You are now in the Mashup Builder interface, where you can drag-and-drop graphical elements (Widgets) to create your GUI.      8. At the top-left, click the Layout tab.        9. Change Positioning to Static. This removes any auto-positioning or dynamic-sizing from our Mashup, so that we can place and size Widgets manually.      10. At the top, click Save again.       Step 3: Add Widgets   We now want to add several Widgets to our Mashup by dragging-and-dropping them into the central Canvas area.   Return to the Widgets tab in the top-left.       2. In the Filter Widgets field, type label.        3. Drag-and-drop five (5) Label Widgets onto the central Canvas area.      4. Select the Label Widgets, go to the Properties in the bottom-left, and change the Label's LabelText to s1_fb1 through s1_fb5.         5. Filter Widgets for text field, and then drag-and-drop five (5) Text Field Widgets onto the central Canvas area.        6. Drag-and-drop two (2) more Label Widgets onto the central Canvas area.      7. Change their LabelText Properties to Result_low_grease and Result_low_grease_mo.        8. Drag-and-drop two (2) more Text Field Widgets onto the central Canvas area.        9. In the top-left Widget's tab, change Category to Legacy.      10. Drag-and-drop an Auto Refresh Widget onto the Canvas.        11. At the top, click Save.         Step 4: Add Data   Now that we have a rough set of Widgets in place to display Data in our Mashup, we need to bring in backend Data from Composer.      1. In the top-right, ensure the Data tab is active.      2. Click the green + button.        3. In the Entity Filter field, search for and select EdgeThing.      4. In the Services Filter field, type getprop.      5. Click the right-arrow beside GetProperties.      6. On the right, check Execute on Load.        7. In the bottom-right, click Done.      8. On the right, expand GetProperties.        9. At the top, click Save.       Step 5: Bind Data   Now that backend Data is accessible inside Mashup Builder, we need to bind it to the various Widgets.   Drag-and-drop Data > Things_EdgeThing > GetProperties > s1_fb1 to the top-left Text Field under the s1_fb1 Label Widget.           2. On the Select Binding Target pop-up, click Text.         3. Repeat for s1_fb2 through s1_fb5 on the 2nd-5th Text Field Widgets.       4. Drag-and-drop Data > Things_EdgeThing > GetProperties > Result_low_grease to the top-right Text Field under the Result_low_grease Label Widget, and select Text on the Binding pop-up.       5. Repeat for Result_low_grease_mo on the Text Field just below.       6. Click the Auto Refresh Widget to select it.       7. In the bottom-left Properties, set RefreshInterval to 1.         8. With the Auto Refresh Widget still selected, click the top-left corner to reveal a drop-down.       9. Drag-and-drop the Refresh Event onto Things_EdgeThing > GetProperties.       10. On the right, click the GetProperties Mashup Data Service to reveal all of its interactions in the bottom-center Connections window.              11. At the top, click Save.       Step 6: View Mashup   In the last guide, we disabled the Analysis Event after confirming that appropriate analytical jobs were being created and completed whenever new data from our EMS Engine Simulator entered Foundation.   We now need to re-enable that Event so that we can see the results in our Mashup.       1. Return to Analytics > Analytics Manager > Analysis Events.       2. Select the Event, and click Enable.   View Analytical Results   With the Analysis Event enabled, we can now view our Mashup to watch engine-failure-predictions be displayed in real time.       1. Return to EFPG_Mashup.       2. At the top, click View Mashup.       3. Wait and notice how the refresh occurs every second, including both true and false analytics results for the "low grease" condition.      4. When you are satisfied that you have observed enough true/false results, return to Analytics > Analytics Manager > Analysis Events and Disable the event.   Through this Learning Path, you have done all of the following:   Connect a remote device to Foundation using the Edge MicroServer (EMS) Fed relevant product-data to the Foundation backend Formatted and exported that data in a manner which Analytics could understand Imported that data and used it to build an Analytics Model of your remote device Started feeding real-time remote data into the model to achieve immediate failure-prediction results Create a GUI to easily visualize those results   You now have a completed Minimum Viable Product (MVP) for a Service Play with MotorCo's new engines.   These new engines may have a known-failure condition, but (because you've instrumented those engines to provide constant data) you can monitor them and predict failures before they actually happen.   This could easily be an up-sell scenario for MotorCo's customers. They could simply purchase the engine. Or they could both purchase the engine and enroll in a service contract where you notified them of impending issues.   What was originally a single-point source of income is now ongoing        Step 7: Next Steps   Congratulations. You've completed the Engine Failure-Prediction GUI guide. In this guide you learned how to:   Create a Mashup Add Widgets to represent different data Bring Backend Data into the Mashup Tie data to Widgets View Analytical Results in a convenient GUI   The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path is Enhanced Engine Failure Visualization.   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Implement Services, Events, and Subscriptions Guide   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Analytics Manager Help Center

Design and Implement Data Models to Enable Predictive Analytics Learning Path   Design and implement your data model, create logic, and operationalize an analytics model.   NOTE: Complete the following guides in sequential order. The estimated time to complete this learning path is 390 minutes.    Data Model Introduction  Design Your Data Model Part 1 Part 2 Part 3  Data Model Implementation Part 1 Part 2 Part 3  Create Custom Business Logic  Implement Services, Events, and Subscriptions Part 1 Part 2  Build a Predictive Analytics Model  Part 1 Part 2 Operationalize an Analytics Model  Part 1 Part 2  

  Operationalize an Analytics Model Guide Part 1   Overview   This project will introduce ThingWorx Analytics Manager. Following the steps in this guide, you will learn how to deploy the model which you created in the earlier Builder guide. We will teach you how to utilize this deployed model to investigate whether or not live data indicates a potential engine failure. NOTE: This guide’s content aligns with ThingWorx 9.3. The estimated time to complete ALL 2 parts of this guide is 60 minutes.    Step 1: Analytics Architecture   You can leverage product-based analysis Models developed using PTC and third-party tools while building solutions on the ThingWorx platform. Use simulation as historical basis for predictive Models Create a virtual sensor from simulation Design-time versus operational-time intelligence It is important to understand how Analytics Manager interacts with the ThingWorx platform.   Build Model   In an IoT implementation, multiple remote Edge devices feed information into the ThingWorx Foundation platform. That information is stored, organized, and operated-upon in accordance with the application's Data Model. Through Foundation, you will upload your dataset to Analytics Builder. Builder will then create an Analytics Model.     Operationalize Model   Analytics Manager tests new data through the use of a Provider, which applies the Model to the data to make a prediction. The Provider generates a predictive result, which is passed back through Manager to ThingWorx Foundation. Once Foundation has the result, you can perform a variety of actions based on the outcome. For instance, you could set up Events/Subscriptions to take immediate and automatic action, as well as alerting stakeholders of an important situation.       Step 2: Simulate Data Source   For any ThingWorx IoT implementation, you must first connect remote devices via one of the supported connectivity options, including Edge MicroServer (EMS), REST, or Kepware Server. Edge Connectivity is outside the scope of this guide, so we'll use a data simulator instead. This simulator will act like an Engine with a Vibration Sensor, as described in Build a Predictive Analytics Model. This data is subdivided into five frequency bands, s1_fb1 through s1_fb5. From this data, we will attempt to predict (through the engine's vibrations) when a low grease emergency condition is occuring.   Import Entities   Import the engine simulator into your Analytics Trial Edition server. Download and unzip the attached amqs_entities.zip file. At the bottom-left of ThingWorx Composer, click Import/Export > Import.     Keep the default options of From File and Entity, click Browse, and select the amqs_entities.twx file you just downloaded.   Click Import, wait for the Import Successful message, and click Close.   From Browse > All, select AMQS_Thing from the list.   At the top, click Properties and Alerts to see the core functionality of the simulator. NOTE: The InfoTable Property is used to store data corresponding to the s1_fb1 through s1_fb5 frequency bands of the vibration sensor on our engine. The values in this Property change every ten seconds through a Subscription to the separate AMQS_Timer Thing. The first set of values are good, in that they do NOT correspond to a low grease condition. The second set of values are bad, in that they DO correspond to a low grease condition. These values will change whenever the ten-second timer fires.   View Mashup   We have created a sample Mashup to make it easier to visualize the data, since analyzing data values in the Thing Properties is cumbersome. Follow these steps to access the Mashup. On the ThingWorx Composer Browse > All tab, click AMQS_Mashup.   At the top, click View Mashup.    Observe the Mashup for at least ten-seconds. You'll see the values in the Grid Advanced Widget change from one set to another at each ten-second interval.     NOTE: These values correspond to data entries from the vibration dataset we utilized in the pre-requisite Analytics Builder guide. Specifically, the good entry is number 20,040... while the bad entry is number 20,600. You can see in the dataset that 20,400 corresponds to a no low grease condition, while 20,600 corresponds to a yes, low grease condition.   Step 3: Configure Provider   In ThingWorx terminology, an Analysis Provider is a mathematical analysis engine. Analytics Manager can use a variety of Providers, such as Excel or Mathcad. In this quickstart, we use the built-in AnalyticsServerConnector, an Analysis Provider that has been specifically created to work seamlessly in Analytics Manager and to use Builder Models. From the ThingWorx Composer Analytics tab, click ANALYTICS MANAGER > Analysis Providers, New....   In the Provider Name field, type Vibration_Provider. In the Connector field, search for and select TW.AnalysisServices.AnalyticsServer.AnalyticsServerConnector.   4. Leave the rest of the options at default and click Save.   Step 4: Publish Analysis Model   Once you have configured an Analysis Provider, you can publish Models from Analytics Builder to Analytics Manager. On the ThingWorx Composer Analytics tab, click ANALYTICS BUILDER > Models.   Select vibration_model_s1_only and click Publish.   On the Publish Model pop-up, click Yes. A new browser tab will open with the Analytics Manager's Analysis Models menu.      4. Close that new browser tab, and instead click Analytics Manager > Analysis Models in the ThingWorx Composer Analytics navigation. This is the same interface as the auto-opened tab which you closed.   False Test   It is recommended to test the published Model with manually-entered sample data prior to enabling it for automatic analysis. For this example, we will use entry 20,400 from the vibration dataset. If the Model is working correctly, then it will return a no low grease condition. In Analysis Models, select the model you just published and click View.   Click Test.   In the causalTechnique field, type FULL_RANGE. In the goalField field, type low_grease. For _s1_fb1 through _s1_fb5, enter the following values: Data Row Name Data Row Value _s1_fb1 161 _s1_fb2 180 _s1_fb3 190 _s1_fb4 176 _s1_fb5 193 6. Click Add Row. 7. Select the newly-added row in the top-right, then click Set Parent Row. 8. Click Submit Job. 9. Select the top entry in the bottom-left Results Data Shape field. 10. Click Refresh Job. Note that _low_grease is false and and _low_grease_mo is well below 0.5 (the threashold for a true prediction).   You have now successfully submitted your first Analytics Manager job and received a result from ThingPredictor. ThingPredictor took the published Model, used the no low grease data as input, and provided a correct analysis of false to our prediction.   True Test Now, let's test a true condition where our engine grease IS LOW, and confirm that Analytics Manager returns a correct prediction. In the top-right, select the false data row we've already entered and click Delete Row. For _s1_fb1 through _s1_fb5, change to the following values: Data Row Name Data Row Value _s1_fb1 182 _s1_fb2 140 _s1_fb3 177 _s1_fb4 154 _s1_fb5 176 3. Select the top entry in the bottom-left Results Data Shape field. 4. Click Refresh Job. Note that _low_grease is true and and _low_grease_mo is above 0.5.                 5. Click Submit Job.         6. Select the top entry in the bottom-left Results Data Shape field.         7. Click Refresh Job. Note that _low_grease is true and and _low_grease_mo is above 0.5.          You've now manually submitted yet another job and received a predictive score. Just like in the dataset Entry 20,600, Analytics Manager predicts that the second s1_fb1 through s1_fb5 vibration frequencies correspond to a low grease condition which needs to be addressed before the engine suffers catastrophic failure.   Enable Model   Since both false and true predictions made by the Model seem to match our dataset, let's now enable the Model so that it can be used for automatic predictions in the future. In the top-left, expand the Actions... drop-down box.   Select Enable.     Click here to view Part 2 of this guide.   

Build a Predictive Analytics Model Guide Part 1   Overview   This project will introduce ThingWorx Analytics Builder. Following the steps in this guide, you will create an analytical model, and then refine it based on further information from the Analytics platform. We will teach you how to determine whether or not a model is accurate and how you can optimize both your data inputs and the model itself.   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete ALL 2 parts of this guide is 60 minutes.      Step 1: Scenario   MotorCo manufactures, sells, and services commercial motors. Recently, MotorCo has been developing a new motor, and they already have a working prototype. However, they've noticed that the motor has a chance to FAIL CATASTROPHICALLY if it's not properly serviced to replace lost grease on a key moving part. In order to prevent this type of failure in the field, MotorCo has decided to instrument their motors with sensors which record vibration. The hope is that these sensors can detect certain vibrations which indicate required maintenance before a failure occurs. MotorCo has decided to utilize ThingWorx Analytics to scan their prototype data for any insights they can gain to address this issue. Challenges: There is a known failure mode of a prototype, but it is not currently possible to predict when the failure might happen Until this failure mode is mitigated, the prototype cannot move into full production While connected data is being monitored, what to do with that data is not currently known The additional sensors are adding to the overall cost of the product   Step 2: Settings   If you manually installed the Analytics extension, then you need to complete the steps below to finalize the connection.  On the ThingWorx Composer Analytics tab, click ANALYTICS BUILDER > Settings. In the Analytics Server field, search for and select your Server Thing Entity, such as AnalyticsServer_Thing or localhost-AnalyticsServer. Click Verify Configuration.   Click Save. WARNING: You MUST CLICK SAVE or the configurations will be lost when you move to Data in the next step.   Step 3: Upload Data   We will now load the data that ThingWorx Analytics will use to generate a model. Download the attached analytics_vibration.zip to your computer. Unzip the analytics_vibration.zip file to access the vibration_data_and_header.csv and vibration_metadata.json files. On the left, click ANALYTICS BUILDER > DATA.   Under Datasets, click New.... The New Dataset pop-up will open.         5. In the Dataset Name field, enter vibration_dataset. 6. In the File Containing Dataset Data section, search for and select vibration_data_and_header.csv. 7. In the File Containing Dataset Field Configuration section, search for and select vibration_metadata.json.   8. Click Submit. Note that it will take a variable amount of time for the data-upload to complete, based on the size of your dataset.              Step 4: Signals   The Signals section of ThingWorx Analytics looks for the most statistically correlated single field in the dataset which relates to your selected goal. This doesn't necessarily indicate that it is the cause of your goal, whether maximizing or minimizing. It just means that the dataset indicates that this single field happens to correlate with the goal that you desire. On the left, click ANALYTICS BUILDER > Signals.   At the top, click New…. A New Signals pop up will open.   3. In the Signal Name field, enter vibration_signal. 4. In the Dataset field, select vibration_dataset. 5. Leave the Goal field set to the default of low_grease. 6. Leave the Filter field set to the default of all_data. 7. Leave the Excluded Fields from Signal field set to the default of empty. 8. Click Submit. 9. Wait ~30 seconds for Signal State to change to COMPLETED The results will be displayed at the bottom.             The results show that the five Frequency Bands for Sensor 1 are the most highly correlated with determining our goal of detecting a low grease condition. For Sensor 2, only bands one and four seem to be at all related, and bands two, three, and five are hardly related at all.   Click here to view Part 2 of this guide. 

    Step 13: C - Support Other Platforms   If you are using a platform that is different than the options provided in the CMake configurations or in the C SDK configurations, you can add your own computer type.   CMake has its own custom toolchain support that enables you to add your computer to the CMake configurations.   Go to the CMake Toolchain. NOTE: The C SDK provides a CMakeList.txt file that contains the setup instructions for the C SDK. Make changes only if you have verified the configurations and compiler information. Create a CMakeList.txt file based on your OS inside the folder in which you would like to setup your application. Modify the Platform Configuration section of the CMakeList.txt file to add your personal computer architecture if it is not already listed. Ensure your configurations are in your application's CMakeList.txt file.   An example of the Platform Configuration section is shown below. if (PLATFORM) if (${PLATFORM} MATCHES "linux-arm") set(OS "linux") set(ARCHITECTURE "arm") set(CMAKE_C_COMPILER ${CMAKE_CURRENT_SOURCE_DIR}/../TOOLS/gcc-linux-arm-cross/bin/arm-angstrom-linux-gnueabi-gcc) set(CMAKE_FIND_ROOT_PATH ${CMAKE_CURRENT_SOURCE_DIR}/../TOOLS/gcc-linux-arm-cross ${CMAKE_CURRENT_SOURCE_DIR}/../TOOLS/gcc-linux-arm-cross/arm-angstrom-linux-gnueabi) # Set LD_LIBRARY_PATH set(Env{LD_LIBRARY_PATH} "${CMAKE_FIND_ROOT_PATH}/lib/gcc") else () if (${CMAKE_SYSTEM_NAME} MATCHES "Linux") set(OS "linux") if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "AMD64") set(ARCHITECTURE "x86_64") set(PLATFORM "linux-x86-64") elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_32" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "i[36]86") set(ARCHITECTURE "x86_32") set(PLATFORM "linux-x86-32") elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv5tejl") set(ARCHITECTURE "arm") set(PLATFORM "linux-arm") elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv[67]l") set(ARCHITECTURE "arm-hwfpu") set(PLATFORM "linux-arm-hwfpu") else () # default to linux-x86_64 set(ARCHITECTURE "x86_64") set(PLATFORM "linux-x86-64") endif() endif ()   An example of how to work with your OS in your application's CMakeList.txt file is shown below: add_executable (SteamSensor src/main.c src/SteamThing.c) target_link_libraries (SteamSensor LINK_PUBLIC twCSdk) if (${OS} MATCHES "windows") add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${CMAKE_BINARY_DIR}/$<CONFIGURATION>/twCSdk.dll" "${CMAKE_CURRENT_BINARY_DIR}/$<CONFIGURATION>/twCSdk.dll" COMMENT "Copying C SDK dll to example directory.") if (${TLS_LIB} MATCHES "openssl") # Copy over openssl dll files add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${OPENSSL_SSLEAY_BIN_PATH}" "${CMAKE_CURRENT_BINARY_DIR}/$<CONFIGURATION>/ssleay32.dll" COMMENT "Copying ssleay dll to example directory.") add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${OPENSSL_LIBEAY_BIN_PATH}" "${CMAKE_CURRENT_BINARY_DIR}/$<CONFIGURATION>/libeay32.dll" COMMENT "Copying ssleay dll to example directory.") endif () endif () if (${OS} MATCHES "linux") if (${TLS_LIB} MATCHES "openssl") # Copy over openssl libraries. add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${OPENSSL_LIB_DIR}/libcrypto.so.1.0.0" "${CMAKE_CURRENT_BINARY_DIR}" COMMAND cmake -E copy_if_different "${OPENSSL_LIB_DIR}/libssl.so.1.0.0" "${CMAKE_CURRENT_BINARY_DIR}" COMMENT "Copying openssl to test directory.") endif () endif () if (${OS} MATCHES "macos") if (${TLS_LIB} MATCHES "openssl") # Copy over openssl libraries. add_custom_command(TARGET SteamSensor POST_BUILD COMMAND cmake -E copy_if_different "${OPENSSL_LIB_DIR}/libcrypto.1.0.0.dylib" "${CMAKE_CURRENT_BINARY_DIR}" COMMAND cmake -E copy_if_different "${OPENSSL_LIB_DIR}/libssl.1.0.0.dylib" "${CMAKE_CURRENT_BINARY_DIR}" COMMENT "Copying openssl to test directory.") endif () endif()     Step 14: Next Steps   Congratulations! You've successfully completed the SDK Reference Guide.   This guide is designed to be used as a reference when developing your application with one of the ThingWorx SDKs.   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Design Your Data Model   Additional Resources   If you have questions, issues, or need additional information, refer to: Resource Link   Community Developer Community Forum   Support C SDK Help Center Java SDK Help Center

  Step 5: Format Timed Values   At the top, click the Services section of scts_thing.   In the Ihnerited Services section, you will see the built-in services of the Statistical Calculation Thing Shape. These services can perform a variety of analytical calculations.   Timed Values Service   The Statistical Calculation Thing Shape can only perform analytics operations on time-series datasets. However, accessing a time-series Value Stream can have a performance hit on the system.   Instead, a Property with an Info Table using a timestamp/value Data Shape is used as the universal input to each built-in service of the Statistical Calculation Thing Shape.   For efficiency, we only reference the Value Stream once to create a formatted timedValues that is used as an input to all other service calls.   At the top, click Services.   Click + Add.   In the Name field, enter timed_values_service. In the Javascript field, copy and past the following code: me.timed_values = me.QueryTimedValuesForProperty({ propertyName: "numbers", maxItems: 10, startTime: me.start_time, endTime: me.end_time });   At the bottom, click Save and Execute.   Click Done, and return to Properties and Alerts. On the timed_values property row, click the pencil icon for Set value of property.   In the pop-up, note that there are now seven entries - each with the 1, 5, 9, 5, 9, 1, and 9 values and the timestamps when you entered them.   In the pop-up, click Cancel. If needed, in the top-right, click the icon to close the slide-out.   Step 6: Calculation Services   Now that the numbers, start_time, end_time, and timed_values service inputs have been set, you can use the built-in Services of the Statistical Calculation Thing Shape to perform a variety of analytics calculations.   Mean Service   First, you will utilize the built-in CalculateMeanValue service.   The dataset is the following: 1, 5, 9, 5, 9, 1, 9.   As such, the mean should be (1+5+9+5+9+1+9)/7 = 39/7 = 5.571...   Return to the Services section. At the top, click + Add. In the Name field, enter mean_service. In the Javascript code section, copy and paste the following: me.mean_result = me.CalculateMeanValue({ timedValues: me.timed_values }); At the top, click Save and Continue.   At the bottom, click Execute. Click Done, then return to the Properties and Alerts section. Note that the mean_result property now has the value 5.571....     Median Service   Next, you will utilize the built-in CalculateMedianValue service.   With our dataset having 5 as the middle value, that should be the result.   Return to the Services section. At the top, click + Add. In the Name field, enter median_service. In the Javascript code section, copy and paste the following: me.median_result = me.CalculateMedianValue({ timedValues: me.timed_values }); At the top, click Save and Continue. At the bottom, click Execute. Click Done, and return to the Properties and Alerts section. Note that the median_result Property now has the value 5.     Mode Service   You will now utilize the built-in CalculateModeValue service.   With the dataset having 9 as the most common value, that should be the result.   Return to the Services section. At the top, click + Add. In the Name field, enter mode_service. In the Javascript code section, copy and paste the following: me.mode_result = me.CalculateModeValue({ timedValues: me.timed_values }); At the top, click Save and Continue.   At the bottom, click Execute. Click Done, and return to the Properties and Alerts section. On the mode_result row and under the Value column, click the "pencil" icon for Set value of property.   In the popup, note that the mode_result Property now has the value 9.   Click Cancel to close the popup. If necessary, at the top-right, click the button to close the slide-out.   Standard Deviation Service   Lastly, you will utilize the built-in CalculateStandardDeviationValue service.   There are multiple free Standard Deviation calculators to check the answer.   Accordingly, the Standard Deviation should be 3.59894...   Return to the Service section. At the top, click + Add. In the Name field, enter standarddev_service. In the Javascript code section, copy and paste the following: me.standarddev_result = me.CalculateStandardDeviationValue({ timedValues: me.timed_values }); At the top, click Save and Continue.   At the bottom, click Execute. Click Done, and return to the Properties and Alerts section. Note that the standarddev_result property now has the value 3.59894...       Step 7: Other Options   The Mean, Median, Mode, and Standard Deviation services you have completed are just a sampling of what the Statistical Calculation Thing Shape offers.   Below is a table of additional built-in services:   Calculation Service Name Description Binned Data Distribution for Bin Size CalculateBinnedDataDistributionForBinSize Calculate the binned distribution of data points based on the desired bin size. Binned Data Distribution for Number of Bins CalculateBinnedDataDistributionForNumberOfBins Calculate the binned distribution of data points based on the desired number of bins. Confidence Interval Values CalculateConfidenceIntervalValues Confidence Interval Values Based on a specified confidence interval percentage, calculate the minimum, median, and maximum interval values. Five Number Property Values CalculateFiveNumberPropertyValues Calculate the five number values: minimum, lower quartile, median, upper quartile, and maximum. Fourier Transform CalculateFourierTransform Calculate the results of running the fast Fourier transform on the specified values. Maximum Value CalculateMaximumValue Calculate the maximum property value in the provided infotable. Minimum Value CalculateMinimumValue Calculate the minimum property value in the provided infotable. Sampling Frequency Values CalculateSamplingFrequencyValues Calculate the sampling frequency values: minimum, median, and maximum.     Step 8: Next Steps   Congratulations!   In this guide, you have learned how to:   Create a Value Stream and Data Shape Create a Thing with the Statistical Calculation Thing Shape Modify a property to record values to the Value Stream Utilize various built-in services for analytical calculations   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Build a Predictive Analytics Model Build Operationalize an Analytics Model   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Descriptive Analytics Help Center

  Step 6: Create OPC UA Tag in ThingWorx   We will now create a Device in Kepware with a Tag whose value will be shown in ThingWorx.   Right-click on Channel1 that was just created, then click Next to accept the default name Device1       Click Next six times to accept the default settings, Then click the Select import items button.     Expand the remote OPC UA server URL, then expand OpcPlc and Telemetry.     Click SlowUint1 to select it, then click Add items >>. Click OK, Next, Finish. Return to ThingWorx Composer. Under Browse > Modeling > Industrial Connections, open IndConn_Server. Expand Channel1 > Device1 > OpcPlc, then click Telemetry.     Click the check-box next to SlowUint1. Click Bind to New Entity.     Select RemoteThing, then click OK. Enter azure-opcua-plc in the Name field, then click Save.     Click Properties and Alerts, then Click Refresh to see the property value changing every 10 seconds.     Step 7: Next Steps   Congratulations! You've successfully completed the Connect to an Azure OPC UA Server guide, and learned how to:   Create an OPC UA Server in Azure Configure Kepware as on OPC UA Client Connect Kepware to ThingWorx Foundation Monitor OPC UA data in ThingWorx Composer   Learn More   We recommend the following resources to continue your learning experience: Capability Guide Experience Create Your Application UI   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Getting Started with ThingWorx Documentation Kepware documentation Support Kepware Support site

  A series of training videos for ThingWorx Analytics   Guide Concept   This guide provides a series of training videos covering ThingWorx Analytics Server and Platform Analytics.   It is recommended that they be viewed in order.    Additionally, a downloadable .zip with additional training materials is provided.     You'll learn how to   Use ThingWorx Analytics Understand the Analytics Thought Process Basic Analytics concepts (data types, variables, modeling) Descriptive Analytics Predictive Analytics modeling techniques Familiarity with other topics: Prescriptive Analytics, Clustering, Time Series, Anomaly Detection Acquire basic knowledge of how to create an end-to-end Smart Application Deepen your Foundation and Analytics understanding NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete all parts of this guide is 12 hours       ThingWorx Analytics Overview   This guide will present a series of training videos for ThingWorx Analytics.It is recommended that you view each guide in-order, as future videos may build on the concepts learned in earlier ones.    Download CourseFiles.zip included in this guide, as it contains important materials for particular videos.   In this course, you will learn the basics of the ThingWorx Analytics Machine Learning process. You will understand how to perform Descriptive Analytics such as identifying Signals, Profiles, or building Clusters.   You will also understand how to train a Model and use Predictive and Prescriptive Scoring. Additional topics include Time Series, Anomaly Detection, and near-real-time Scoring. You will also learn about how to create a simple smart application using Analytics together with other pieces of the overall ThingWorx platform.     ThingWorx Analytics Video Guide   Module 1: ThingWorx Analytics Overview Module 2: Use Case Discussion Module 3: Data Profiling Module 4: Data Transformation and Feature Engineering Module 5: Descriptive Analytics Module 6: Predictive Models and Model Validation Module 7: Scoring Predictive Realtime Prescriptive Module 8: Time Series Modeling Module 9: Anomaly Detection Module 10: ThingWorx Foundation and Analytics Integration Module 11: Mini Project     Next Steps   Congratulations! You've successfully completed the Analytics Training Videos guide, and learned how to:   Use ThingWorx Analytics Understand the Analytics Thought Process Basic Analytics concepts (data types, variables, modeling) Descriptive Analytics Predictive Analytics modeling techniques Familiarity with other topics: Prescriptive Analytics, Clustering, Time Series, Anomaly Detection Acquire basic knowledge of how to create an end-to-end Smart Application Deepen your Foundation and Analytics understanding   Additional Resources If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Analytics Support Help Center          

  Build a Predictive Analytics Model - Video Guide   This project will introduce ThingWorx Analytics Builder via a convenient video-guide. Following the steps in this video, you will create an analytical model, and then refine it based on further information from the Analytics platform. We will teach you how to determine whether or not a model is accurate and how you can optimize both your data inputs and the model itself.     ThingWorx Analytics Server Installation with SSL   This video demonstrates the installation of Analytics Server with SSL protections enabled. It includes information about generating the necessary certificates and truststores to enable SSL for each component that connects with the server.     ThingWorx Platform Analytics Installation with SSL   This video demonstrates the Platform Analytics installation with SSL protections enabled. It provides information about generating the necessary certificates and truststores to enable SSL for all connected components, including RabbitMQ and Flink.     Toolbar Widget | ThingWorx 9   Watch this video to learn how to add a Toolbar widget to a mashup in ThingWorx. You'll learn how to define actions using a data service and create bindings to control and configure a Grid widget.     ThingWorx SSO: Login demonstration from Azure AD to ThingWorx   This video demonstrates the ThingWorx SSO login procedure using Azure AD. The login procedure is shown from a user point of view. Then a behind the scenes view looks at the design in ThingWorx Composer.     Menu Bar Widget ThingWorx 9   Watch this video to learn how to create a mashup layout that uses a Menu Bar widget for navigation. You'll learn how to create a layout, define menu items, and configure the widget.     ThingWorx AD FS SSO Setup   This video provides a walk-through of the steps required to set up SSO for ThingWorx in an environment where AD FS is both the CAS and the IdP. The focus is on the AD FS setup steps.     Create Your Application UI   Following the steps in this video-guide, you will learn how to use the ThingWorx Mashup Builder tool to create a Graphical User Interface (GUI) for your IoT Application. We will teach you how to rapidly create and update a Mashup, which is a custom visualization built to display data from devices according to your application's business and technical requirement     Get Started with ThingWorx for IoT   Explore the ThingWorx Foundation Internet-of-Things application building platform in a convenient, instructional video guide format.     Thingworx Mashup 101 - Do's and Don'ts   This session covers the most common and useful tips about how to correctly use Mashup builder, Widgets and Layouts – and what to avoid - to create applications with good principles of UI/UX and easier to maintain.     What's New in ThingWorx 9.1   The industry’s most complete IIoT platform just got better. Loaded with a full range of new and updated features and functionality, you can expect powerful platform capability enhancements across the board.       Standardize Connectivity to Devices, Applications, & Systems for Centralized IIot Data   You can’t implement IIoT solutions if you can’t connect to your assets, but in complex environments connectivity can seem like an insurmountable challenge. ThingWorx makes it easy to establish standardized connectivity, so you can create a secure, single-source for accessing industrial data across your IT and OT systems     Bar Chart Widget | ThingWorx 9   Watch the following video on how to add the Bar Chart widget to a mashup and configure basic widget properties.     Button Widget | ThingWorx 9   Watch the this video to learn how to add the Button widget to a mashup and use its Clicked event to trigger data services.     Line Chart Widget | ThingWorx 9   Watch the this video to learn how to add the Line Chart widget to a mashup and bind a data source.     ReImagine Your Application UI With Collection and Custom CSS   Create compelling, modern application user interfaces in ThingWorx with the latest enhancements to our Mashup visualization platform - Collection and Custom CSS. In this webinar with IoT application designer Gabriel Bucur, we'll show how the new Collection widget makes it easy to replicate visual content in your UI for menu systems, dashboards, tables, and more.     Testing Your Edge Application   Native testing is an often-overlooked aspect of edge application development. This session shares how to perform thorough testing before you push your application into live production.     Predictive Maintenance with Thingworx 101   Adopting a predictive maintenance strategy for the first time can often feel daunting, but it doesn't have to. Learn how ThingWorx can be used to leverage edge devices and turn monitoring activity into action.     ThingWorx Mashup Pitfalls: What to Avoid and What Not to Do   Designing Mashups takes time. Moreover, expanding upon a Mashup that was built for a small-scale application can get cumbersome.     IoT Security: Keeping Devices Safe in a High Risk World   Understanding access controls can be difficult. With the built-in security features and concepts in ThingWorx, you can easily give your devices access on-demand.     Edge Connectivity in Unreliable Networks   The Store and Forward feature within ThingWorx Industrial Connectivity assures users that critical data collected at the edge won't get lost during an outage.  

Getting Started on the ThingWorx Platform Learning Path   Learn hands-on how ThingWorx simplifies the end-to-end process of implementing IoT solutions.   NOTE: Complete the following guides in sequential order. The estimated time to complete this learning path is 210 minutes.   Get Started with ThingWorx for IoT   Part 1 Part 2 Part 3 Part 4 Part 5 Data Model Introduction Configure Permissions Part 1 Part 2 Build a Predictive Analytics Model  Part 1 Part 2

In ThingWorx Analytics, you have the possibility to use an external model for scoring. In this written tutorial, I would like to provide an overview of how you can use a model developed in Python, using the scikit-learn library in ThingWorx Analytics. The provided attachment contains an archive with the following files: iris_data.csv: A dataset for pattern recognition that has a categorical goal. You can click here to read more about this dataset TestRFToPmml.ipynb: A Jupyter notebook file with the source code for the Python model as well as the steps to export it to PMML RF_Iris.pmml: The PMML file with the model that you can directly upload in Analytics without going through the steps of training the model in Python The tutorial assumes you already have some knowledge of ThingWorx and ThingWorx Analytics. Also, if you plan to run the Python code and train the model yourself, you need to have Jupyter notebook installed (I used the one from the Anaconda distribution). For demonstration purposes, I have created a very simple random forest model in Python. To convert the model to PMML, I have used the sklearn2pmml library. Because ThingWorx Analytics supports PMML format 4.3, you need to install sklearn2pmml version 0.56.2 (the highest version that supports PMML 4.3). To read more about this library, please click here Furthermore, to use your model with the older version of the sklearn2pmml, I have installed scikit-learn version 0.23.2.  You will find the commands to install the two libraries in the first two cells of the notebook.   Code Walkthrough The first step is to import the required libraries (please note that pandas library is also required to transform the .csv to a Dataframe object):   import pandas from sklearn.ensemble import RandomForestClassifier from sklearn2pmml import sklearn2pmml from sklearn.model_selection import GridSearchCV from sklearn2pmml.pipeline import PMMLPipeline   After importing the required libraries, we convert the iris_data.csv to a pandas dataframe and then create the features (X) as well as the goal (Y) vectors:   iris_df = pandas.read_csv("iris_data.csv") iris_X = iris_df[iris_df.columns.difference(["class"])] iris_y = iris_df["class"]   To best tune the random forest, we will use the GridSearchCSV and cross-validation. We want to test what parameters have the best validation metrics and for this, we will use a utility function that will print the results:   def print_results(results): print('BEST PARAMS: {}\n'.format(results.best_params_)) means = results.cv_results_['mean_test_score'] stds = results.cv_results_['std_test_score'] for mean, std, params in zip(means, stds, results.cv_results_['params']): print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))   We create the random forest model and train it with different numbers of estimators and maximum depth. We will then call the previous function to compare the results for the different parameters:   rf = RandomForestClassifier() parameters = { 'n_estimators': [5, 50, 250], 'max_depth': [2, 4, 8, 16, 32, None] } cv = GridSearchCV(rf, parameters, cv=5) cv.fit(iris_X, iris_y) print_results(cv)   To convert the model to a PMML file, we need to create a PMMLPipeline object, in which we pass the RandomForestClassifier with the tuning parameters we identified in the previous step (please note that in your case, the parameters can be different than in my example). You can check the sklearn2pmml  documentation  to see other examples for creating this PMMLPipeline object :   pipeline = PMMLPipeline([ ("classifier", RandomForestClassifier(max_depth=4,n_estimators=5)) ]) pipeline.fit(iris_X, iris_y)   Then we perform the export:   sklearn2pmml(pipeline, "RF_Iris.pmml", with_repr = True)   The model has now been exported as a PMML file in the same folder as the Jupyter Notebook file and we can upload it to ThingWorx Analytics.   Uploading and Exploring the PMML in Analytics To upload and use the model for scoring, there are two steps that you need to do: First, the PMML file needs to be uploaded to a ThingWorx File Repository Then, go to your Analytics Results thing (the name should be YourAnalyticsGateway_ResultsThing) and execute the service UploadModelFromRepository. Here you will need to specify the repository name and path for your PMML file, as well as a name for your model (and optionally a description)   If everything goes well, the result of the service will be an id. You can save this id to a separate file because you will use it later on. You can verify the status of this model and if it’s ready to use by executing the service GetDetails:   Assuming you want to use the PMML for scoring, but you were not the one to develop the model, maybe you don’t know what the expected inputs and the output of the model are. There are two services that can help you with this: QueryInputFields – to verify the fields expected as input parameters for a scoring job   QueryOutputFields – to verify the expected output of the model The resultType input parameter can be either MODELS or CLUSTERS, depending on the type of model,    Using the PMML for Scoring With all this information at hand, we are now ready to use this PMML for real-time scoring. In a Thing of your choice, define a service to test out the scoring for the PMML we have just uploaded. Create a new service with an infotable as the output (don’t add a datashape). The input data for scoring will be hardcoded in the service, but you can also add it as service input parameters and pass them via a Mashup or from another source. The script will be as follows:   // Values: INFOTABLE dataShape: "" let datasetRef = DataShapes["AnalyticsDatasetRef"].CreateValues(); // Values: INFOTABLE dataShape: "" let data = DataShapes["IrisData"].CreateValues(); data.AddRow({ sepal_length: 2.7, sepal_width: 3.1, petal_length: 2.1, petal_width: 0.4 }); datasetRef.AddRow({ data: data}); // predictiveScores: INFOTABLE dataShape: "" let result = Things["AnalyticsServer_PredictionThing"].RealtimeScore({ modelUri: "results:/models/" + "97471e07-137a-41bb-9f29-f43f107bf9ca", //replace with your own id datasetRef: datasetRef /* INFOTABLE */, });   Once you execute the service, the output should look like this (as we would have expected, according to the output fields in the PMML model):   As you have seen, it is easy to use a model built in Python in ThingWorx Analytics. Please note that you may use it only for scoring, and the model will not appear in Analytics Builder since you have created it on a different platform. If you have any questions about this brief written tutorial, let me know.

With ThingWorx, we can already use univariate anomaly alerts (on a single sensor value). However, in many situations, the readings from an individual sensor may not tell you much about the overall issue and a multivariate anomaly detector can be more useful. This post is intended to provide an overview of the Azure Anomaly Detector and how it can be integrated with ThingWorx. The attachment contains: A document with detailed instructions about the setup; A .csv file with the multivariate timeseries dataset; A .twx file with some entities that need to be imported in ThingWorx as well as the CSVParser extension that needs to be installed; A .zip file that will need to uploaded in an Azure Blob Container at some point in the setup

In our interactions with PTC customers we often learn they have previously performed Analytics modeling in Python, Matlab, R, or even built home grown analyses in languages such as Java or C++. As expected, when adopting an Industrial Innovation Platform such as ThingWorx that also has its own ThingWorx Analytics module, customers do not want to reimplement everything from scratch and would rather integrate their previous work in the Smart Applications built in ThingWorx, leveraging a combination of their existing toolset together with ThingWorx Analytics modeling. That is certainly possible and there are multiple ways to do that. In this article we will focus on several general ways to make that happen, but it is important to keep in mind that language specific approaches are also possible and we are happy to discuss those in the specific context of the customer.   Here are five different ways to bring existing Analytics into ThingWorx: If the task is to reuse an existing predictive model developed in a language such as Python/R/Matlab, typically one can export that model in PMML (Predictive Model Markup Language), an xml format, and import it in ThingWorx Analytics using the AnalyticsServer_ResultsThing -> UploadModel service. Libraries such as sklearn2pmml & r2pmml can be utilized towards that goal. The imported model can then be used in the same fashion as a ThingWorx Analytics developed model to power smart applications built in ThingWorx. If the Analysis involves more complex tasks than Predictive Modeling, such as custom data normalizations or non-standard Machine Learning models or home grown algorithms, one can use the options below. Call the ThingWorx exposed REST Web API from Python/Matlab/R/Java/Javascript. Every service from ThingWorx can be called that way, and the API can also be used to push analyses results into ThingWorx for further consumption, perhaps together with other sources of data such as sensor readings, in the smart applications built there. The documentation for the ThingWorx REST API can be found here.  Expose the existing Analytics via using a thin layer of REST Web Services. For example, in Python, this can be done using Flask, with few lines of code. Then, the orchestration can happen from ThingWorx by calling the exposed Web Service and weaving the results back into smart applications. Often our customers' current architecture involves a relational database (e.g. SQL Server, Oracle, etc) that is powering the existing Analytics, and stores the end results (predictions, correlations, etc). In this scenario, we can connect ThingWorx directly to that database to read these results.  Finally, in the case of complex Analytics, where a tighter integration with ThingWorx is desired, existing Analytics / algorithms can be wrapped into a ThingWorx Extension or an Analytics Provider using the corresponding PTC SDKs.  When choosing an integration option, customers need to carefully balance complexity of integration, constraints of their architecture, Analytics modeling complexity, as well as end user consumption requirements.

Analytics projects typically involve using the Analytics API rather than the Analytics Builder to accomplish different tasks. The attached documentation provides examples of code snippets that can be used to automate the most common analytics tasks on a project such as: Creating a dataset Training a Model Real time scoring predictive and prescriptive Retrieving the validation metrics for a model Appending additional data to a dataset Retraining the model The documentation also provides examples that are specific to time series datasets. The attached .zip file contains both the document as well as some entities that you need to import in ThingWorx to access the services provided in the examples. 

Checkout the below video which explores the Creo As A Service (CAAS) feature of the Product Insight extension. This allows to retrieve Creo analysis computation inside ThingWorx through the Analytics Manager framework.  

Time series prediction uses a model to predict future values based on previously observed values. Time series data differs somewhat from non-time series data in both the formatting of the data and the training of predictive models. This article will highlight several important considerations when dealing with time series data. Preparing Time Series Data: The data must contain exactly one field with Op Type “TEMPORAL” and one field with Op Type “ENTITY_ID”, which defines the identifier for an entity, such as a machine serial number. The ENTITY_ID field should remain the same as long as there are no missing timestamps and it is within the same asset but should be different for different assets or asset runs in order to accurately assign history during model training and scoring.     The TEMPORAL field is a numeric field indicating the order of the data rows for a specific entity . One should also ensure that data is formatted such that the timestamps are equally spaced (for example, one data point every minute) and that no gaps exist in the sequence of numbers.   If there are gaps in the time series data, it is recommended to restart the series after the gap as a new entity. Alternatively, if the gap is small enough (few data points), linear interpolation based on the gap endpoint values within the same entity is generally acceptable.   Model Creation in Time Series: When creating a timeseries model in Analytics Builder, you will be asked to specify a lookback size and lookahead parameter. The lookback size determines how many historical datapoints (including the current row) will be used in the model. The lookahead indicates how many time steps ahead to predict.  If the value of the goal variable is not known at time of scoring, unchecking Use Goal History will use the goal column during training but not its history during scoring.   Time Series models can also be created in Services using the Training Thing. The lookback size and lookahead parameter are specified in the CreateJob service. The virtualSensor field is used to indicate if the model should be trained to predict values for a field that will not be available during scoring. For example, one can train a time series model to predict Volume using evolving Temperature and Pressure, based on sensor data for these three variables over a period of time. However, the Volume sensor may be removed from further assets in order to reduce costs, and the predictive model can be used instead.   Two important considerations: ThingWorx Analytics will expand historical data in the time series into new columns. This process creates new features using the values of the previous time steps. Additionally, low order derivatives, together with average and standard deviation features are computed over small contiguous subgroups of the historical data.   The expansion process can make the dataset exceptionally wide, so time series training is generally significantly slower compared to training with no history on the same dataset. This gets exacerbated when lookback size = 0 (auto-windowing, a process where the system is trying to find the optimal lookback). If there are columns that are not changing or change infrequently (such as a device serial number or zip code of the device’s location), these should be marked as Static when importing the data. Any columns labeled Static will not be expanded to create new features. Care also needs to be taken to exclude any features that are known to not be relevant to the prediction. Using a large lookback can eliminate how many examples / entities the model has available to train. For example, if a lookback of 8 is used, then any entities that have less than 8 examples will not be used in training. For the same reason, scoring for time series produces less results than the number of rows provided as input: if 10 rows are provided and lookback is 6, then only 5 predictions will be produced.

With the advent of faster and cheaper hardware, and owing to vast improvements in connectivity such as Gigabit networks and 5G, data is collected and processed at an ever increasing pace. As such, there is a related need for the Analytical Models built on top of such data to evolve over time. As part of this evolution, modelling experiments with more data, new variables, new techniques, and different training parameters will be performed. The resulting models need to be tracked, monitored, and appropriate versions need to be used for scoring in various scenarios. This creates the need for version control of Analytical Models.   ThingWorx Analytics makes it easy to implement an “Analytics Model Version Control” system via two mechanisms: A job Id and timestamp based identification system, so if a model with the same jobName (model name) is retrained, the old model will still exist and can be easily retrieved A tagging mechanism for the above jobs   Before using the above techniques to version control Analytics Models, it is critical to decide what represents “the same model” to be versioned. Unlike in the world of Software Engineering where the concept to be versioned is a file / folder that evolves over time, in the world of Analytics Models, there could be several, potentially customer specific definitions. For example, one definition could be “same training parameters, but trained on the latest, most comprehensive data available”. Yet another, more relaxed definition could be “any training parameters, but same training dataset and goal” or even “any training parameters, on any historical version of the training dataset”.   Once this concept is agreed upon within the customer's organization, and if training is done in a ThingWorx service by calling the APIs, for a given jobName (model name) one can simply query the tags for a LatestVersion type tag, increment, and create the new model with the same jobName and incremented tag. Any model version with the same jobName and its corresponding performance metrics can then be accessed using the tag. Additional tags (such as techniques used, dataset version, etc) can be added if desired to make retrieval of context dependent models more efficient.

The intend of this post. This post is for the user who want to validate that, the ThingWorx Analytics Services related to Confidence Models work successfully. Underneath video walk through the steps to validate the Services via a non-supported PTC Mashup. The intend of this video is uniquely to validate that, Services related to Confidence Models works successfully.  What package files are used in the video? The Mashup entities and dataset used in the video, is attached to this post. Feel free to download the files and test on your machine. Why use Confidence Models? A confidence model is a way of adding confidence interval information to a predictive model. Statistically, for a given prediction, a confidence model provides an interval with upper and lower bounds, within which it is confident, up to a certain level, that the actual value occurs. During predictive scoring, this measure of confidence provides additional information about the accuracy of the prediction. More information about Confidence Models can be found here at PTC Help Center 

This demo walks through how Range Count works. The Range Count service calculates the difference between the maximum and minimum value.  Agenda of the demo: 1. Create a demo Thing 2. Add a new property to the Thing 3. Add the property statistical calculation type Range Count to the Thing 4. Validate the statistical calculation service via the added calculation type Range Count 5. Validate the statistical calculation service via the Service QueryTimedValuesForProperty