IoT & Connectivity Tips

  Step 6: Record Data   Now that we have a place to permanently store the values coming from the EMS engine simulator, we'll write a Service to take samples and place them within the Info Table.   Part of that Service, though, will be incrementing the identifier, so we'll need to create one last Property.   Ensure that you're on the Properties and Alerts tab of EdgeThing. At the top-left, click + Add.     In the Name field of the slide-out on the right, type identifier. Change the Base Type to NUMBER. Click Persistent. Click Has Default Value. In the Has Default Value field, type 0.   At the top-right, click the "Check" button for Done. At the top, click Save.   Store the Property Values   With all the pieces in place, we can now create our Service to add entries to our Info Table Property.   At the top of EdgeThing, click Services.   At the top-left, click + Add.   On the "+ Add" drop-down, select Local (JavaScript). In the Service Info > Name field, type recordService.     Expand Me/Entities > Properties.     Click the arrow beside infoTableProperty.     Type .AddRow({ after me.infoTableProperty to being the process of calling the "AddRow()" function.     We now have called the function which will add a row of information to the Info Table Property, one entry for each column of the formatting Data Shape.   We just need to specify which values go into which column.   Add the following lines to store the individual Identifier count into the first column of the Info Table Property: identifier:me.identifier, Because we want the identifier in the stored data to increment on each run, and we want to start the count at 1 (and the Default Value is 0), add the following line to the top of the Service: me.identifier=me.identifier+1;       Add the low_grease value with the following line: low_grease:me.low_grease, Add the following lines to store the five frequency bands of the first sensor: s1_fb1:me.s1_fb1, s1_fb2:me.s1_fb2, s1_fb3:me.s1_fb3, s1_fb4:me.s1_fb4, s1_fb5:me.s1_fb5, Add the final lines to store the five frequency bands of the second sensor and close out the AddRow() function: s2_fb1:me.s2_fb1, s2_fb2:me.s2_fb2, s2_fb3:me.s2_fb3, s2_fb4:me.s2_fb4, s2_fb5:me.s2_fb5 }); You completed Service should look like the following: me.identifier=me.identifier+1; me.infoTableProperty.AddRow({ identifier:me.identifier, low_grease:me.low_grease, s1_fb1:me.s1_fb1, s1_fb2:me.s1_fb2, s1_fb3:me.s1_fb3, s1_fb4:me.s1_fb4, s1_fb5:me.s1_fb5, s2_fb1:me.s2_fb1, s2_fb2:me.s2_fb2, s2_fb3:me.s2_fb3, s2_fb4:me.s2_fb4, s2_fb5:me.s2_fb5 });     At the top, click Done. At the top, click Save.       Run the Service   With our Service completed, let's run it to store a sampling of the data coming from our EMS Engine Simulator.   Under the Execute column in the center, on the recordService row, click the "Play" icon for Execute service. At the bottom-right, click Execute.     At the bottom-right, click Done. At the top, click Properties and Alerts.   Under the Value column, on the infoTableProperty row, click the "Pencil" icon for Set value of property.   Note that the Service has captured a snap-shot of the vibration data and grease condition and permanently stored it within the Info Table Property. You now have not only an Engine Simulator that is constantly sending data from a remote EMS, but a way to permanently record data at points that you deem significant.   Feel free to return to the Service and call it several more times. Each time, the values coming from the Engine Simulator will be stored in another entry in the Info Table Property.       Step 7: Next Steps   Congratulations! You've successfully completed the Use the EMS to Create an Engine Simulator guide, and learned how to:   Modify an EMS Template Provision Thing Properties and Values from an EMS rather than Foundation Send information from an EMS to Foundation Store large amounts of data in an InfoTable Property Create a simulator for testing   The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path is Engine Simulator Data Storage. Learn More We recommend the following resources to continue your learning experience: Capability Guide Build Engine Simulator Data Storage Build Implement Services, Events and Subscriptions Additional Resources If you have questions, issues, or need additional information, refer to: Resource Link Community Developer Community Forum Support Analytics Builder Help Center  

  Enhance your Engine Failure-Prediction GUI.     GUIDE CONCEPT   This guide will use ThingWorx Foundation’s Mashup Builder to create a more advanced Graphical User Interface (GUI) than the one we originally created to display results from Analytics Manager’s engine-failure predictions.   Following the steps in this guide, you will learn how to utilize Widgets and backend data to more completely visualize customer failure conditions.       YOU'LL LEARN HOW TO   Create a Mashup with a Header Divide your Mashup into Sub-sections Use a Contained Mashup to reuse development Store historical data in a Value Steam Display historical data in a Time Series Chart Show spreadsheet data via a Grid Widget Tie Mashup controls into the ThingWorx backend   NOTE: The estimated time to complete all parts of this guide is 60 minutes.     Step 1: Scenario   In this guide, we’re taking our previous MotorCo Minimum Viable Product (MVP) Mashup and expanding it.   Our original Mashup showed the results from ThingWorx Analytics as it determined whether or not a low-grease condition was currently present.   The goal of this guide is to create an Enhanced GUI to visualize those predicted “low grease” conditions in a more complete manner.     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   Just like in the last guide, we're now going to create a Mashup to visualize ThingWorx Analytics results.   This one is simply going to be more complicated to include additional functionality.   But before we can start designing our GUI, we must first instantiate a Mashup onto which we can place our Widgets.       1. In ThingWorx Foundation, click Browse > Visualization > Mashups.         2. Click + New.       3. On the New Mashup pop-up under Responsive Templates, click Header Only.       4. Click OK.       5. In the Name field, type EEFV_Mashup.       6. If Project is not already set, search for and select PTCDefaultProject.       7. At the top, click Save.       8. At the top, click Design.         Step 3: Set Layout   Now that we’re in Mashup Builder, you can see the separate top-section of the central Canvas area created by our selection of “Header Only” on the New Mashup pop-up.   Unlike the original Mashup where we used Static Positioning, most of this Mashup will continue to use Responsive so that it can grow and shrink as resolution changes on various viewing devices.   To add multiple Responsive Widgets to a Responsive Positioning Mashup, though, you need to create some additional sub-sections. We’ll do so now.       1. In the top-left of Mashup Builder, click the Layout tab.       2. Click the main, bottom section of the Canvas, i.e. the non-header section, to select it.       3. On the Layout tab, click Add Top.       4. With the top-half of the original bottom section still selected, click Add Left.       5. Click in the bottom section to select the bottom-half of the original container.       6. Click Add Left.       7. At the top, click Save.      You now have a Responsive Positioning Mashup with five (5) sub-sections, i.e. :   Header Top-left Top-Right Bottom-left Bottom-right       Step 4: Adjust Header    In this step, we'll outfit the Header sub-section with a company name and logo.       1. Select the top Header section and ensure that you're still on the Layout tab in the top-left.         2. Change the Positioning to Static.       3. In the top-left, select the Widgets tab.       4. Drag-and-drop an Image Widget onto the Header section.       5. Expand the size of the Image Widget by dragging the corners.       6. Drag-and-drop a Label Widget onto the Header section.       7. Expand the size of the Label Widget.       8. With the Label Widget still selected, change the LabelText Property (in the bottom-left) to MotorCo, and hit the keyboard Tab key to lock-in your modification.         9. In the bottom-left, change to the Style Properties tab.       10. Expand Base > Label, and change font-size to 72px.       11. At the top, click Save.   Upload Media Image   We want to set the earlier Image Widget to the company logo.   To do so, we need to upload it to Foundation by creating a Media Entity.       1. Click Browse > Visualization > Media.       2. Click + New.       3. In the Name field, type EEFV_Logo.       4. If Project is not already set, search for and select PTCDefaultProject.       5. Right-click and "Save as" to download motorco-logo.jpg.       6. Under Image, click Change.       7. Navigate to and select the motorco-logo.jpg file you just downloaded.       8. Click Open.       9. At the top, click Save.   Change Image to Logo   Now that we have the company logo stored within ThingWorx, we can update the Image Widget to reference it.       1. Return to EEFV_Mashup.       2. Click the Image Widget to select it, and ensure that the bottom-left Properties tab is active.         3. Scroll down in the Properties until you find SourceURL.         4. In the Search Media field, type eefv.         5. Select EEFV_Logo.         6. Click Save.     Change Background Color   Finally, we want to change the background color of the Header.       1. In the top-left, select the Explorer tab. Note that the Explorer tab may be in the top-left drop-down if you're using a lower-resolution screen.         2. Select the Header itself.         3. In the bottom-left, select the Style Properties tab and expand Base > Container.         4. Beside background, click the white square to open a color-selector.       5. Select a color you desire.         6. Click Select.         7. Click Save.     Click here to view Part 2 of this guide.

  Step 4: Simulated Model   Models are primarily used by Analytics Manager (which will be discussed in the next guide), but they can still be used to estimate the accuracy of predictions.   When Models are calculated, they inherently withhold a certain amount of data (~20%). The prediction model is then run against the withheld data. This provides a form of "accuracy measure".   The withheld-data is selected randomly, so you'll actually get a slightly different model and accuracy measure each time that you create a Model versus the same dataset.   On the left, click Analytics Builder > Models.   Click New….   In the Model Name field, enter simulated_model. In the Dataset field, select simulated_dataset.   Click Submit. After ~60 seconds, the Model Status will change to COMPLETED.     Select the model that was created in the previous step, i.e. simulated_model. Click View… to open the Model Information page.   As with Signals and Profiles, our Model is once again "too good". In fact, it's perfect.   The expected "Precision" is 1.0, i.e. 100%. The True vs False Positive rate shown in the graph goes straight up to the top immediately.   While you want a graph that is "high and left", you're very unlikely to ever see real-world scenarios such as shown here.   Still, you've been able to progress the process of using Foundation (and now Analytics) to build an analytical model of MotorCo's prototype engine.   What needs to happen now is to receive real world data from your R&D engineers.     Step 5: Upload Real World Data   In your process of using the EMS Engine Simulator, the idea has always been to get a headstart on the engine developers.   At some point, they would wire sensors into the EMS and start providing real world data.   In our scenario, that has now happened. Real world data is being fed from the EMS to Foundation, Foundation is collecting that data in an Info Table Property, and you've even exported the data as a .csv. file.   This new dataset is over periods of both good and bad grease conditions. The engineers monitoring the process can flip a sensor switch connected to the EMS to log the current grease situation as either good or bad at the same time that the vibration sensors are taking readings.   We will now load this real world dataset into Analytics in the same manner that we did earlier with the simulated dataset.   Download the attached analytics_vibration.zip file 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....   In the Dataset Name field, enter vibration_dataset. In the File Containing Dataset Data section, search for and select vibration_data_and_header.csv. In the File Containing Dataset Field Configuration section, search for and select vibration_metadata.json.   Click Submit.     Step 6: Real World Signals and Profiles   Now that the real-world vibration data has been uploaded, we’ll re-run Signals and Profiles just as we did before.   Hopefully, we’ll start seeing some patterns.   On the left, click Analytics Builder > Signals. At the top, click New….   In the Signal Name field, enter vibration_signal. In the Dataset field, select vibration_dataset.   Click Submit. Wait ~30 seconds for Signal State to change to COMPLETED     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 related, while bands two, three, and five are hardly relevant at all.   This is a very different result than our earlier simulated data. Instead, it looks like it’s possible that the vibration-frequencies getting pickup up by our first sensor are explicitly more important.   Profiles   We’ll now re-run Profiles with our real-world dataset. On the left, click Analytics Builder > Profiles. Click New….   In the Profile Name field, enter vibration_profile. In the Dataset field, select vibration_dataset.   Click Submit. After ~30 seconds, the Signal State will change to COMPLETED.     The results show several Profiles (combinations of data) that appear to be statistically significant.   Only the first few Profiles, however, have a significant percentage of the total number of records. The later Profiles can largely be ignored.   Of those first Profiles, both Frequency Bands from Sensor 1 and Sensor 2 appear.   But in combination with the result from Signals (where Sensor 1 was always more important), this could possibly indicate that Sensor 1 is still the most important overall.   In other words, since Sensor 1 is statistically significant both by itself and in combination (but Sensor 2 is only significant in combination  with Sensor 1), then Sensor 2 may not be necessary.     Click here to view Part 3 of this guide.

    Step 5: Create InfoTable   Now that we have connected values coming from our EMS engine simulator, we want a method of permanent storage whenever we feel it's appropriate to take a sample.   From repeated sampling, we'll be able to build up a historical record usable for both manual inspection, as well as automatic analysis via ThingWorx Analytics (though ThingWorx Analytics is beyond the scope of this guide).   To hold these records, we'll use an Info Table Property.   But any time that you create an Info Table, you first need a Data Shape to format the columns.   Click Browse > MODELING > Data Shapes.     At the top-left, click + New.   In the Name field, type esimDataShape.     If Project is not already set, search for and select PTCDefaultProject. At the top, click Field Definitions.     We now want to add a separate Field Definition for each entry of our engine simulator data, i.e. low_grease, s1_fb1 through s1_fb5, and s2_fb1 through s2_fb5.   In addition, we'll add an additional field named identifier which simply keeps a rolling count of the current log entry number.   Click + Add.     In the Name field on the right slide-out, type identifier Change the Base Type to NUMBER. Check Is Primary Key   At the top-right, click the "Check with a +" button for Done and Add.     Repeatedly add additional definitions as per the chart below: Note that you will NOT check the "Is Primary Key" box, as you only need one, i.e. identifier. Name Base type low_grease NUMBER s1_fb1 NUMBER s1_fb2 NUMBER s1_fb3 NUMBER s1_fb4 NUMBER s1_fb5 NUMBER s2_fb1 NUMBER s2_fb2 NUMBER s2_fb3 NUMBER s2_fb4 NUMBER Create one additional entry for s2_fb5 and NUMBER, but click the "Check" button for DONE. At the top, click Save.     Create Info Table   Now that we have a Data Shape we can add an Info Table Property to EdgeThing. Return to the Properties and Alerts tab of EdgeThing.   At the top-left, click + Add.   In the Name field of the slide-out on the right, type infoTableProperty.   Change the Base Type to INFOTABLE.   In the new Data Shape field, search for and select esimDataShape.   Check the Persistent checkbox.   At the top-right, click the "Check" button for Done. At the top, click Save.     Click here for Part 4 of this guide.

  Step 7: Real World Model   We’ll now rerun model creation with the Real World data.   Even though Signals and Profiles are possibly telling us that only Sensor 1 is needed, the first Model you’ll create will contain all the data, while the second Model will exclude Sensor 2. We’ll then compare the Models to see which one is going to work the best for predicting engine failures.   On the left, click Analytics Builder > Models. Click New….   In the Model Name field, enter vibration_model. In the Dataset field, select vibration_dataset.   Click Submit. After ~60 seconds, the Model Status will change to COMPLETED. Select the model that was created in the previous step, i.e. vibration_model. Click View… to open the Model Information page. Note that your model may differ slightly from the picture below, as the automatically-withheld "test" data is randomly chosen.       Unlike our simulated dataset, this real-world data is not perfect. However, it’s still pretty good, and is much more representative of what a real-world scenario would indicate.   The True Positive Rate shown on the Receiver Operating Characteristic (ROC) chart are much higher than the False Positives.   The curve is relatively high and to the left, which indicates a high accuracy level.   You may also click on the Confusion Matrix tab in the top-left, which will show you the number of True Positive and True Negatives in comparison to False Positives and False Negatives.     NOTE: The number of correct predictions is much higher than the number of incorrect predictions.   As such, we now know that our Sensors have a relatively good chance at predicting an impending failure by detecting low grease conditions before they cause catastrophic engine failure.   Refined Model   We can now compare this first Model that includes both Sensors to a Model using only Sensor 1, since we suspect that Sensor 2 may not be necessary to achieve our goal. On the left, click Analytics Builder > Models. Click New…. In the Model Name field, enter vibration_model_s1_only. In the Dataset field, select vibration_dataset.   On the right beside Excluded Fields from Model, click the Excluded Fields button.   Select s2_fb1 through s2_fb5.   While all the s2 values are selected, click the green "right-arrow", i.e. > button, in the middle.   At the bottom-left, click Save.   Click Submit. After ~60 seconds, the Model State will change to COMPLETED. With vibration_model_s1_only selected, click View….     The ROC chart is comparable to the original model (including Sensor 2).   Likewise, the Confusion Matrix (on the other tab) indicates a good ratio of correct predictions versus incorrect predictions.     NOTE: These Models may vary slightly from your own final scores, as what data is used for the prediction versus for evaluation is random.   ThingWorx Analytics’s Models have indicated that you are likely to receive roughly the same accuracy of predicting a low-grease condition whether you use one sensor or two!   If we can get an accurate early-warning of the low grease condition with just one sensor, it then becomes a business decision as to whether the extra cost of Sensor 2 is necessary.     Step 8: Next Steps   Congratulations! You've successfully completed the Build an Engine Analytical Model guide, and learned how to:   Load an IoT dataset Generate machine learning predictions Evaluate the analytics output to gain insight   The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path is Manage an Engine Analytical Model.   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Analyze Operationalize an Analytics Model Build Implement Services, Events, and Subscriptions Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Analytics Builder Help Center

  Step 5: Contained Mashup   Our Minimum Viable Product (MVP) Mashup which we created in the last guide did have valid information.   Being able to display the inputs coming from the engine, as well as the analytical results coming from ThingWorx Analytics, are certainly items we don’t want to lose in this new, more complete Mashup.   Rather than recreating that work from scratch, we’ll simply include that previous Mashup in one of our sub-section via the Contained Mashup Widget.       1. Click on the top-left section to select it, and ensure that you’re on the Widgets tab in the top-left.       2. Drag-and-drop a Contained Mashup Widget onto the top-left section.       3. With the Contained Mashup Widget selected, return to the Properties tab in the bottom-left.       4. Scroll down and locate the Name Property.       5. Search for and select EFPG_Mashup       6. Click Save.     Add Column Labels   The original Mashup we created (and have now embedded in the new one) had some labels for the inputs and outputs. However, you had to know what things like “s1_fb1” meant to understand that that was an input.   We can go back to the original EPFG_Mashup, make some modifications for greater clarity, and those changes will also carry over to our new Mashup.       1. Reopen the old EPFG_Mashup on the Design tab.       2. Move all of the Widgets down to leave some extra room at the top.       3. Drag-and-drop two Divider Widgets onto the Canvas above both the Inputs and Results columns.       4. Select a Divider Widget, and go to its Style Properties.       5. Expand Base > Line to reveal the background Style Property.       6. Click on the default gray color to see the available options.       7. Choose the built-in black at the bottom, and click Select.       8. Make the same modification to the other Divider Widget.       9. Drag-and-drop two more Label Widgets onto the Canvas above the two columns.       10. Change their LabelText Properties to Inputs and Results, respectively.     Change Background and Size       1. From the Explorer tab in the top-left, select the container.       2. Select the Style Properties tab in the bottom-left and expand Base > Container.       3. Change the background Style Property to a color you prefer.       4. With the container still selected in the Explorer tab, drag the corners of the Mashup to reduce its size.       5. You could even move the Results column over, place the Auto Refresh Widget underneath, and then reduce the container size even further.       6. Click Save.     View Mashup Thus Far   With the changes to the previous EFPG_Mashup now complete, let’s ensure that everything carried over to our new Mashup.       1. Return to EEFV_Mashup.       2. Click Save.       3. Click View Mashup.   Note how the various changes we made to the base Mashup are also being shown, via a Contained Mashup Widget, in our new Mashup.   Splitting out functionality to a separate Mashup that is then embedded where needed is a great way to re-use content and simplify development.       Step 6: Add Chart   Our original Mashup (which has now been embedded in our new one) shows the instantaneous analytical results based on the inputs coming from the Edge MicroServer (EMS).   However, when investigating remote customer issues, it might be helpful to see some historical trends. A temporary "blip" of a low-grease indication might be worrisome, but it may not require immediate intervention unless the issue was occuring consistently or for extended periods of time.   Fortunately, creating a historical record is relatively simple in ThingWorx Foundation.   All that is really needed is a place in which to store the past records.   One of the easiest such storage methods is a Value Stream.       1. In ThingWorx Foundation, click Browse > Data Storage > Value Streams.       2. Click + New.       3. On the Choose Template pop-up, select ValueStream and click OK.       4. In the Name field, type EEFV_ValueStream.       5. If Project is not already set, search for and select PTCDefaultProject.       6. At the top, click Save.     Link Value Stream and Begin Storage   Now that we have a Value Stream to act as a storage location, we want to link it to EdgeThing.   After EdgeThing knows where to store historical data, we can simply instruct it which Property we want to archive by setting it to Logged.       1. Return to EdgeThing and its General Information tab.       2. In the Value Stream field, search for and select EEFV_ValueStream.       3. Click Save.       4. Still on EdgeThing, click Properties and Alerts.       5. Click Result_low_grease_mo to trigger the slide-out from the right-side.         6. Check Logged.       7. Click the Check icon in the top-right to close the slide-out.       8. Click Save.     Add Line Chart and Data   As per most guides in this Learning Path, it is assumed that you have an active connection to the EMS Engine Simulator and have your Analytics Event currently set to active.   This provides both the engine-sensor inputs and the analytical results for our Mashup.   After adding the Value Stream above, you'll need to let it run for a bit for the historical data to be archived. After it's run for a while and we have a valid history build-up, you can display that history in a Line Chart.       1. Return to EEFV_Mashup on the Design tab.       2. Click on the top-right section to select it.         3. From the Widgets tab, drag-and-drop a Line Chart onto the top-right section.         4. In the top-right of Mashup Builder, ensure the Data tab is selected.         5. Click the green + button.         6. On the Add Data pop-up in Entity Filter, search for and select EdgeThing.       7. In Services Filter, type queryprop.       8. Click the right arrow button besides QueryPropertyHistory.       9. Check Execute on Load.         10. Click Done.       11. Expand Data > Things_EdgeThing > QueryPropertyHistory > Returned Data.       Bind Data and View Mashup   Now that we have both our method of displaying the historical data, i.e. a Line Chart, as well as a method to bring backend data into the Mashup, i.e. QueryPropertyHistory, we can bind them together and see how our Mashup is progressing.       1. From the right under the Data tab, drag-and-drop EdgeThing > QueryPropertyHistory > Returned Data > All Data onto the Line Chart in the top-right of the Canvas.         2. On the Select Binding Target pop-up, click Data.         3. With the Line Chart selected, explore its Properties in the bottom-left.       4. Change XAxisField to timestamp.         5. Click Save.       6. Click View Mashup.     Your own Line Chart will vary depending on what values your Engine Simulator is sending to Foundation and Analytics.   NOTE: Remember that the Analysis Event needs to be Enabled for new values to be fed into Result_low_grease_mo.     Click here to view Part 3 of this guide.  

  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

This video continues Module 3: Data Profiling of the ThingWorx Analytics Training videos. It describes metadata, and how it is used to ensure that your data is handled appropriately when running Signals, Profiles, Training, Scoring, and other jobs inside ThingWorx Analytics.

  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

    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