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There have been a number of questions from customers and partners on when they should use different tools for calculation of descriptive analytics within ThingWorx applications. The platform includes two different approaches for the implementation of many common statistical calculations on data for a property: descriptive services and property transforms. Both of these tools are easy to implement and orchestrate as part of a ThingWorx application. However, these tools are targeted for handling different scenarios and also differ in utilization of compute resources. When choosing between these two approaches it is important to consider the specific use case being implemented along with how the implemented approach will fit into the overall design and architecture of the ThingWorx environment. This article will provide some guidance on scenarios to use each of these approaches in ThingWorx applications and things to consider with each approach.   Let's look at the two different approaches and some guidelines for when they should be used.   Descriptive services (click for more details) provide a set of ThingWorx services to analyze a data set and perform many common data transformations.  These services are targeted for performing calculations and transformations on recent operating history of a single property.  Descriptive services are called on demand to perform batch calculations. Scenarios to use descriptive services: On demand calculations performed within a mashup, a service call or an event to determine action and calculation results are not (always) stored Regular occurring calculations on logged property values or generated datasets (batch calculations) Calculations are done regularly in minutes, hours or days on a discrete set of data.  Examples: average value in last hour, median value in last day, or max value in last half hour.  Time between data creation and analysis is minutes or hours.  Some latency in the calculation result is acceptable for the use case. Input data set has 10s to 100s to 1000s of values.  Keep the size of the input data at 10,800 values or less.  If larger data sizes are required, then break them into micro batches if possible or use other tools to handle the processing. Multiple calculations need to be done from the same set of input data.  Examples: average value in last hour, max value in the last hour and standard deviation value in the last hour are all required to be calculated. Things to consider when using descriptive services Requires input dataset to be in the specific datashape format that is used by descriptive services.  If property values are logged in a value stream, there is a service to query the values and prepare the dataset for processing.  If scenarios where the data is not for a logged property, then another service or sql query can be used to prepare the dataset for processing. Requires javascript development work to implement.   This includes creation of a service to execute the descriptive services and usage of subscriptions and events to orchestrate calculations. An example of the javascript to execute descriptive services is available in the help center (here) Typically retrieval of the input data from value stream (QueryTimedValuesForProperty) is slowest part of the process. The input data is sent to an out of process platform analytics service for all calculations. Broader set of calculation services available (see table at the end of this article) Remember that these services are not meant to be used for big data calculations or big data preparation.  Look for other approaches if the input data sets grow larger than 10,800 values Property Transforms (click for more details) provide a set of transformation services for streaming data as it enters ThingWorx.   Property transforms are targeted for performing continuous calculations on recent values in the stream of a single property and delivering results in (near) real-time.  Since property transforms are continuous calculations, they are always running and using compute resources. Before implementing property transforms review the information in the property transform sizing guide to better understand factors that impact the scaling of property transforms. Scenarios to use: Continuous calculations on a stream for a single property as new data comes into ThingWorx New values enter the stream faster than one value per minute (as a general guideline) Calculations required to be done in seconds or minutes.  Examples: average electrical current in last 10 seconds, median pressure in the last 10 readings,  or max torque in last minute Time between data creation and analysis is small (in seconds).  Results of property transform is required for rapid decisions and action so reducing latency is critical Data sets used for calculation are small and contain 10s to 100s of values.  Calculated results are stored in a new property in the ThingModel Things to consider when using property transforms Codeless process to create new property transforms on a single property in the ThingModel Does not require input property values to be logged as calculations are performed on streaming data as it enters ThingWorx Unlike descriptive services which only execute when called, each property transform creates a continuously running job that will always be using compute resources.  Resource allocations for property transforms must be included in the overall system architecture.  Before selecting the property transform approach, refer to the Property Transform Sizing Guide for more information about how different parameters affect the performance of Property Transforms and results of performance load test scenarios. Let’s apply these guidelines to a few different use cases to determine which approach to select. 1. Mashup application that allows users to calculate and view median temperature over a selected time window In this scenario, the calculation will be executed on-demand with a user defined time window. Descriptive services are the only option here since there is not a pre-defined schedule and the user can select which data to use for the calculation.   2. Calculate the max torque (readings arriving one per second) on a press over each minute without storing all of the individual readings. In this scenario, the calculation will be executed without storing the individual readings coming from the machine. The transformation is made to the data on its way into ThingWorx and continuously calculating based on new values. Property transforms are the only option here since the individual values are not being stored.   3. Calculation of average pressure value (readings arriving one per second) over a five minute window to monitor conditions and raise an alert when the median value is more than two standard deviations from expected. In this scenario, both descriptive services and property transforms can perform the calculation required. The calculation is going to occur every 5 minutes and each data set will have about 300 values. The selection of batch (descriptive services) or streaming (property transforms) will likely be determined by the usage of the result. In this case, the calculation result will be used to raise an alert for a specific five minute window which likely will require immediate action. Since the alert needs to be raised as soon as possible, property transforms are the best option (although descriptive services will handle this case also with less compute resource requirements).   4, Calculation of median temperature (readings each 20 seconds) over 48 hour period to use as input to predict error conditions in the next week. In this scenario, the calculation will be performed relatively infrequently (once every 48 hours) over a larger data set (about 8,640 values). Descriptive services are the best option in this case due to the data size and calculation frequency. If property transforms were used, then compute resources would be tied up holding all of the incoming values in memory for an extended period before performing a calculation. With descriptive services, the calculation will only consume resource when needed, or once every 48 hours.   Hopefully this information above provides some more insight and guidelines to help choose between property transforms and descriptive services. The table below provides some additional comparisons between the two approaches.     Descriptive Services Property Transforms Purpose Provide a set of ThingWorx services to analyze a data set and perform many common data transformations. Provide a set of prescribed transformation services for streaming data as it enters ThingWorx. Processing Mode Batch Streaming / Continuous Delivery API / Service Composer interface API / Service Input Data Discrete data set Must be logged Single property Configurable by time or lookback Rolling data set on property X Persistence is optional Single property Configurable by time or lookback Output Data Return object handled programmatically Single output for discrete data set New property f_X in the input model Continuous output at configurable frequency Output time aligned with input data Available Services Statistics (min, max, mean, median, mode, std deviation) SPC calculations (# continuous data points: above threshold, in / out of range, increasing / decreasing, alternating) Data distribution: count by bins (histogram) Five numbers (min, lower quartile, median, upper quartile, max) Confidence interval Sampling frequency Frequency transform (FFT) Statistics (min, max, mean, median, mode, std deviation) SPC calculations (# continuous data points: above threshold, in / out of range, increasing / decreasing, alternating)
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    Step 3: Monitor SPC Properties   Once the properties have been configured, you are ready to progress on to SPC monitoring.   This is done by viewing the PTC.SPC.Monitoring_MU Mashup.   NOTE: Once a property has been configured for SPC monitoring, 2-5 minutes must pass before the accelerator has collected enough streaming values to perform SPC calculations. During this time, you may see an item that has 2 OOC rules violated; “Collecting Enough Data to Begin Monitoring” and “… to Compute Capability”.     Within Property SPC Status, click an item from the grid that has 0 in the OOC column. See that the Out of Control Rules area is blank, since there are no currently-violated SCP rules for that selected property. Both CP and CPK are above 1.0, showing that the process is capable. All the points in the charts are blue, as they do not violate any OOC rules. Within Property SPC Status, click an item from the grid that has a nonzero number in the OOC column.   See that the Out of Control Rules area has one or more rules listed that are currently being violated. Here, the rule being violated is Range Out of Control. One or more of the charts may have point(s) that are red. Here, you can see one of the points in the R chart is red, as it is above the Upper Control Limit. In the bottom-left, click Refresh Now.   Note that the datetime value of the date picker in the lower-left indicates the period to which the Mashup has updated. It is possible that Properties that were previously not in violation are now violating OOC rules; the opposite is also possible. Note that not all points that violate an OOC rule will display in red. Here, you can see that the rule 8 Consecutive Points Above Center Line is violated, but the corresponding points in the X-Bar Chart are not red. View the SPC status at a time from the recent past. In the lower-left, click date picker. Choose a datetime of 2-3 minutes in the past. You will see that the date picker reflects the datetime value you selected. All the Widgets in the Mashup are updated to reflect their status at that point in time. Enter continuous monitoring mode. Select any of the eight (8) items from the Property SPC Status area. To the left of the Refresh Now button, click the toggle. This will enable the Mashup to auto-refresh every 30 seconds. In the upper-right corner of the Property SPC Status area, click the gray arrow to expand the monitoring display.   Every 30 seconds, the monitoring display will update to show the most recent status for the item you selected.       Step 4: Next Steps   Congratulations! You’ve successfully completed the Deploy Statistical Process Control guide, and learned how to:   Configure multiple properties for SPC monitoring Identify abnormalities in streaming property values   Learn More   We recommend the following resources to continue your learning experience:    Site              Link Wikipedia Western Electric Rules Wikipedia Process Capability   Additional Resources If you have questions, issues, or need additional information, refer to:    Resource       Link Community Developer Community Forum Support ThingWorx Help Center  
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    Configure streaming asset properties for SPC monitoring.   Guide Concept   Note: This guide is intended only as a starting point and is not a fully developed or supported solution. This accelerator has been developed using ThingWorx 8.5 and should not be used with any previous versions of the software.   This project introduces you to configuring Properties from your connected streaming assets for Statistical Process Control (SPC) monitoring.   Following the steps in this guide, you will learn how multiple connected assets and their Properties can be displayed in a hierarchy tree.   You will then configure these Properties using predetermined set points, and upper and lower control points for the assets.   Finally, you will learn to navigate the monitoring of the Properties.   We introduce some of the basic building blocks of an SPC accelerator, including important Things and Mashups. You will also use ThingWorx Timers to simulate streaming data.     You'll learn how to   Configure multiple properties for SPC monitoring Identify abnormalities in streaming property values   NOTE: The estimated time to complete this guide is 30 minutes     Step 1: Import SPC Accelerator   Before exploring Statistical Process Control (SCP) within ThingWorx Foundation, you must first import some Entities via the top-right Import / Export button.   Download and unzip PTC_StatisticalCalculations_PJ.zip and PTC_SPC_PJ.zip. These two files each contain a ThingWorx project of a similar name. Import PTC_StatisticalCalculations_PJ.twx first. Import PTC_SPC_PJ.twx once the other import has completed. Explore the imported entities.   Each of the projects contain multiple entities of various types. The most important entities you will use in this guide are as follows:    Entity Name                                   Description Motor_Pump1 Timer to simulate streaming data Motor_Blower1 Timer to simulate streaming data PTC.SPC.ConfigurationHelper Thing that manages the accelerator PTC.SPC.Configuration_MU Mashup for configuring SPC properties PTC.SPC.Monitoring_MU Mashup for monitoring SPC properties   Step 2: Configure Properties for SPC monitoring   You may configure SPC monitoring for multiple production lines, connected assets on those lines, and time-series Properties on those assets using the SPC accelerator.   This is done by viewing the PTC.SPC.Configuration_MU Mashup.   Follow these steps to configure an SPC Property.   Create a new production line   In the Enter New Production Line Name text field, type Line100. Click Add New Line.   Now you will see the new production line added to the Asset Hierarchy tree along the left. All production lines you’ve created (as well as their assets and the assets’ Properties) will be shown here.   In the lower-right, the SPC Property Configuration area has disappeared because the item selected in the Asset Hierarchy tree is the new line; only assets within lines can have streaming Properties.   Add a streaming asset to Line100   Within the Select Asset Thing entity picker, type Motor_Blower1. Click Add Asset for Line.   This asset has Properties that are streaming into ThingWorx. Multiple assets can be added to the same production line by selecting the line from the Asset Hierarchy tree and following the steps above.   If you select the new asset from the Asset Hierarchy tree, you will see that the list of Properties Eligible for SPC Monitor is shown in the SPC Property Configuration area. All Properties have the same default configuration associated with them.   Configure a property for SPC Monitoring   Below is a brief description of each of the configuration parameters:    Parameter           Description Sample Size Number of consecutive property values to average together. For example, a Sample Size of 5 will tell the accelerator to group every 5 property values together and calculate their average value. XBar Points Number of the most recent sample aggregations to display in the SPC Monitoring Mashup. This also affects SPC calculations. Capability Points Number of the most recent sample aggregations to use when populating the Capability Histogram in the SPC Monitoring Mashup. Set Point Value determined to be the set point for that particular property on the asset. Lower Design Spec Value determined to be the lower design spec for that particular property on the asset. This is used for capability calculations. Upper Design Spec Value determined to be the upper design spec for that particular property on the asset. This is used for capability calculations.   Select Pressure1 from the list of eligible properties. Enter the following values:  Properties                      Values Sample Size 5 Xbar Points 30 Capability Points 60 Set Point 73 Lower Design Spec 68 Uppder Design Spec 78   3, Select Xbar-R Chart. 4. Click Add or Update SPC Monitoring. 5. Pressure1 is added to the Asset Hierarchy tree underneath the Motor_Blower1 asset. 6. If you select this Property, you can modify the configuration and save it by clicking Add or Update SPC Monitoring.   Configure assets and Properties   Follow these steps using the following parameters:                                     Line Asset  Property  Sample Size Xbar Point Capability Points Set Points Lower Design Spec Upper Design Spec Chart Type 100 Motor_Blower1 Pressure1 5 30 60 73 68 78 Xbar-R 100 Motor_Blower1 Pressure2 5 30 60 78 68 89 Xbar-R 100 Motor_Blower1 Temperature1 5 30 60 50 44 56 Xbar-s 100 Motor_Blower1 Temperature2 5 30 60 85 73 97 Xbar-s 100 Motor_Pump1 Vibration10 5 30 60 150 108 190 Xbar-s 100 Motor_Pump1 Vibration11 8 60 100 200 168 220 Xbar-s 100 Motor_Pump1 Pressure100 5 30 60 100 84 118 Xbar-R 100 Motor_Pump1 Pressure200 5 30 60 90 84 97 Xbar-R   As you add assets to Line100 and configure their Properties, you will see the Asset Hierarchy tree grow. If you need to remove an asset or its associated Properties from the Asset Hierarchy tree, you may select that item, and click Remove Selected. For any item you remove, its child-items will also be removed.   Click here to view Part 2 of this guide.
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  PLEASE NOTE DataConnect has now been deprecated and no longer in use and supported.   We are regularly asked in the community how to send data from ThingWorx platform to ThingWorx Analytics in order to perform some analytics computation. There are different ways to achieve this and it will depend on the business needs. If the analytics need is about anomaly detection, the best way forward is to use ThingWatcher without the use of ThingWorx Analytics server. The ThingWatcher Help Center is an excellent place to start, and a quick start up program can be found in this blog. If the requirement is to perform a full blown analytics computation, then sending data to ThingWorx Analytics is required. This can be achieved by Using ThingWorx DataConnect, and this is what this blog will cover Using custom implementation. I will be very pleased to get your feedback on your experience in implementing custom solution as this could give some good ideas to others too. In this blog we will use the example of a smart Tractor in ThingWorx where we collect data points on: Speed Distance covered since last tyre change Tyre pressure Amount of gum left on the tyre Tyre size. From an Analytics perspective the gum left on the tyre would be the goal we want to analyse in order to know when the tyre needs changing.   We are going to cover the following: Background Workflow DataConnect configuration ThingWorx Configuration Data Analysis Definition Configuration Data Analysis Definition Execution Demo files   Background For people not familiar with ThingWorx Analytics, it is important to know that ThingWorx Analytics only accepts a single datafile in a .csv format. Each columns of the .csv file represents a feature that may have an impact on the goal to analyse. For example, in the case of the tyre wear, the distance covered, the speed, the pressure and tyre size will be our features. The goal is also included as a column in this .csv file. So any solution sending data to ThingWorx Analytics will need to prepare such a .csv file. DataConnect will perform this activity, in addition to some transformation too.   Workflow   Decide on the properties of the Thing to be collected, that are relevant to the analysis. Create service(s) that collect those properties. Define a Data Analysis Definition (DAD) object in ThingWorx. The DAD uses a Data Shape to define each feature that is to be collected and sends them to ThingWorx Analytics. Part of the collection process requires the use of the services created in point 2. Upon execution, the DAD will create one skinny csv file per feature and send those skinny .csv files to DataConnect. In the case of our example the DAD will create a speed.csv, distance.csv, pressure.csv, gumleft.csv, tyresize.csv and id.csv. DataConnect processes those skinny csv files to create a final single .csv file that contains all these features. During the processing, DataConnect will perform some transformation and synchronisation of the different skinny .csv files. The resulting dataset csv file is sent to ThingWorx Analytics Server where it can then be used as any other dataset file.     DataConnect configuration   As seen in this workflow a ThingWorx server, DataConnect server and a ThingWorx Analytics server will need to be installed and configured. Thankfully, the installation of DataConnect is rather simple and well described in the ThingWorx DataConnect User’s guide. Below I have provided a sample of a working dataconnect.conf file for reference, as this is one place where syntax can cause a problem:   ThingWorx Configuration The platform Subsystem needs to be configured to point to the DataConnect server . This is done under SYSTEM > Subsystems > PlatformSubsystem:     DAD Configuration The most critical part of the process is to properly configure the DAD as this is what will dictate the format and values filled in the skinny csv files for the specific features. The first step is to create a data shape with as many fields as feature(s)/properties collected.   Note that one field must be defined as the primary key. This field is the one that uniquely identifies the Thing (more on this later). We can then create the DAD using this data shape as shown below   For each feature, a datasource needs to be defined to tell the DAD how to collect the values for the skinny csv files. This is where custom services are usually needed. Indeed, the Out Of The Box (OOTB) services, such as QueryNumberPropertyHistory, help to collect logged values but the id returned by those services is continuously incremented. This does not work for the DAD. The id returned by each services needs to be what uniquely identifies the Thing. It needs to be the same for all records for this Thing amongst the different skinny csv files. It is indeed this field that is then used by DataConnect to merge all the skinny files into one master dataset csv file. A custom service can make use of the OOTB services, however it will need to override the id value. For example the below service uses QueryNumberPropertyHistory to retrieve the logged values and timestamp but then overrides the id with the Thing’s name.     The returned values of the service then needs to be mapped in the DAD to indicate which output corresponds to the actual property’s value, the Thing id and the timestamp (if needed). This is done through the Edit Datasource window (by clicking on Add Datasource link or the Datasource itself if already defined in the Define Feature window).   On the left hand side, we define the origin of the datasource. Here we have selected the service GetHistory from the Thing Template smartTractor. On the right hand side, we define the mapping between the service’s output and the skinny .csv file columns. Circled in grey are the output from the service. Circled in green are what define the columns in the .csv file. A skinny csv file will have 1 to 3 columns, as follow: One column for the ID. Simply tick the radio button corresponding to the service output that represents the ID One column representing the value of the Thing property. This is indicated by selecting the link icon on the left hand side in front of the returned data which represent the value (in our example the output data from the service is named value) One column representing the Timestamp. This is only needed when a property is time dependant (for example, time series dataset). On the example the property is Distance, the distance covered by the tyre does depend on the time, however we would not have a timestamp for the TyreSize property as the wheel size will remain the same. How many columns should we have (and therefore how many output should our service has)? The .csv file representing the ID will have one column, and therefore the service collecting the ID returns only one output (Thing name in our smartTractor example – not shown here but is available in the download) Properties that are not time bound will have a csv file with 2 columns, one for the ID and one for the value of the property. Properties that are time bound will have 3 columns: 1 for the ID, one for the value and one for the timestamp. Therefore the service will have 3 outputs.   Additionally the input for the service may need to be configured, by clicking on the icon.   Once the datasources are configured, you should configure the Time Sampling Interval in the General Information tab. This sampling interval will be used by DataConnect to synchronize all the skinny csv files. See the Help Center for a good explanation on this.   DAD Execution Once the above configuration is done, the DAD can be executed to collect properties’ value already logged on the ThingWorx platform. Select Execution Settings in the DAD and enter the time range for property collection:   A dataset with the same name as the DAD is then created in DataConnect as well as in ThingWorx Analytics Server Dataconnect:     ThingWorx Analytics:   The dataset can then be processed as any other dataset inside ThingWorx Analytics.   Demo files   For convenience I have also attached a ThingWorx entities export that can be imported into a ThingWorx platform for you to take a closer look at the setup discussed in this blog. Attached is also a small simulator to populate the properties of the Tractor_1 Thing. The usage is : java -jar TWXTyreSimulatorClient.jar  hostname_or_IP port AppKey For example: java -jar TWXTyreSimulatorClient.jar 192.168.56.106 8080 d82510b7-5538-449c-af13-0bb60e01a129   Again feel free to share your experience in the comments below as they will be very interesting for all of us. Thank you
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This video is Module 11: ThingWorx Analytics Mashup Exercise of the ThingWorx Analytics Training videos. It shows you how to create a ThingWorx project and populate it with entities that collectively comprise a functioning application. 
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    Use Analytics Manager to automatically perform engine analytical calculations.   Guide Concept   This guide will use ThingWorx Analytics Manager to compare external-data from an Edge MicroServer (EMS) "Engine Simulator" to a previously-built analytical model.   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.   You'll learn how to   Deploy and execute computational models Define and trigger Analysis Events Map incoming data to the Model Map analytic outputs to applications   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 60 minutes       Step 1: Scenario   In this guide, we're continuing the same 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.   The goal of this guide is to now operationalize that previously-created model to analyze individual, external readings to see if the "low grease" condition is currently happening.     Analytical model creation can be extremely helpful for the automotive 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 put an analytic model of your engine into service to actively monitor performance.       Step 2: Configure Provider   In ThingWorx terminology, an Analysis Provider is a mathematical analysis engine.   Analytics Manager can use a variety of Providers, such as Excel, Mathcad, or even Analytics Server pre-built ones.   In this guide, we use the built-in AnalyticsServerConnector, a Provider that has been specifically created to work seamlessly in 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.   Leave the rest of the options at default and click Save.     Step 3: 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. Close that new browser tab, and instead click Analysis Models in the ThingWorx Composer Analytics navigation. This is the same interface as the auto-opened tab which you closed.   Click the model to select it. At the top, click Enable. Note the pop-up indicating that the Enable was successful.       Step 4: Modify EdgeThing   In previous guides in this Vehicle Predictive Pre-Failure Detection Learning Path, you have created various Entities, including Things such as EdgeThing.   In order to automate the process of pushing data from EdgeThing to Analytics Manager, we need to add a few more Properties to EdgeThing.   These Properties are simple STRING variables, and we'll also set Default Values for them to configure parameters of Analytics Manager.   The first is causalTechnique, which tells Analytics Manager which criteria to use when measuring the impact of a feature on a range of goal values.   The second is goalField, which is simply the data field for which Analytics Manager should try to identify the correlation. In this case, it'll be our primary issue, i.e. low_grease.   It is not mandatory that these suggested Property names match, but they are the names used within ThingWorx Analytics. You could use any Property name you wanted, as you'll be mapping from a particular Property to the functionality within Analytics in a later step.   Return to EdgeThing > Properties and Alerts.   Click + Add.   In the Name field, type causalTechnique. Check Has Default Value. In the text field under "Has Default Value", type FULL_RANGE. Note that you MUST type FULL_RANGE, including capitalization, as that is a command within Analytics Server.   Click the "Check with a +" icon for Done and Add. In the Name field, type goalField. Check Has Default Value. In the text field under "Has Default Value", type low_grease. Note that you MUST type low_grease, including being all lower-case, as that is the exact name of the Analytics Server goal.   Click the "Check" icon for Done. Click Save.   Results Storage   We also need a place in which to store the results that Analytics Manager returns. We'll utilize a few additional Properties for that as well.   On the EdgeThing > Properties and Alerts tab, click + Add. In the Name field, type Result_low_grease. Check the Base Type to BOOLEAN. Check Persistent.   Click the "Check with a +" icon for Done and Add. In the Name field, type Result_low_grease_mo. Change the Base Type to NUMBER. Check Persistent.   Click the "Check" icon for Done. Click Save.       Step 5: Create Event   Events are automatic analysis jobs which are submitted based on a pre-defined condition. In this step, we'll configure an Analysis Event, which will execute automatically whenever there is a data-change in our simulated engine.   On the ThingWorx Composer Analytics tab, click Analytics Manager > Analysis Events.   Click New.... If not already selected, change Source Type (Required) to Thing.   In Source, search for and select EdgeThing. In Event, select DataChange. In Property, select s1_fb1. If there are multiple s1_fb1 Properties, select the first one, as the second one is the s1_fb1 entry in the Info Table Property. In Provider Name, select Vibration_Provider. In Model Name, select the published Model.   Click Save.       Click here to view Part 2 of this guide.  
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    Step 6: Map Data   Now that the event is created, we need to map the Properties of EdgeThing to the fields required to invoke an Analysis Job.   We'll start with the Inputs.   Select the previously-created Event, and click Map Data....   Click Inputs Mapping.   In Source Type, select Thing. In Source, search for and select EdgeThing.   On the left, scroll down and select s1_fb1. Note that you do NOT want the s1_fb1 that is part of the InfoTable Property, because the Info Table Property only stores recorded data, not live data. On the right, select _s1_fb1, the first frequency band required for the Model to make a prediction.   Click the Map button in the center.   Repeat this mapping process for for s1_fb2 through s1_fb5.   Map causalTechnique to causalTechnique in the same manner. This is a String Property in EdgeThing with a Default Value of "FULL_RANGE". Map goalField to goalField in the same manner. This is a String Property in EdgeThing with a Default Value of "low_grease".   Map Results   Now that the Inputs are mapped, we also want to map the Results.   Click Results Mapping on the left.   Map _low_grease to Result_low_grease. Map _low_grease_mo to Result_low_grease_mo.   Click Close to close the mapping pop-up.   Enable Event   Now that we've done the mapping from Foundation to Analytics, let's Enable the Analysis Event so that it can automatically generate and process Analysis Jobs. Select the mapped Analysis Event. Select Enable.   Now that you have enabled the Analysis Event, the new data will be submitted to Analytics Manager whenever EdgeThing's s1_fb1 Property changes.   An Analysis Job will automatically run, with a predictive score sent back and stored in EdgeThing's Result_low_grease (Boolean) and Result_low_grease_mo (Number) Properties.     Step 7: Check Jobs   In this step, we'll confirm that the automatic analysis of information coming from remote devices is operational.   On the ThingWorx Composer Analytics tab, click Analytics Manager > Analysis Jobs.   Uncheck Filter Completed Jobs.   Select a Job and click View.... Click Results.   NOTE: You will see true or false, corresponding to either a low grease or no low grease condition. Using this technology, you could create a paid customer service, where you offered to monitor remote engines, in return for automatically shutting them down before they experience catastrophic engine failure.   For that example implementation, you would utilize the EdgeThing.Result_low_grease BOOLEAN Property to trigger other actions.   For instance, you could create an Alert Event which would be triggered on a true reading.   You could then have a Subscription which paid attention to that Alert Event, and performed an action, such as sending an automatic shutdown command to the engine when it was experiencing a likely low grease event.   NOTE: We recommend that you return to the ThingWorx Composer Analytics > Analytics Manager > Analysis Events tab and Disable the Event prior to continuing. Since the simulator generates an Event every ~1 seconds, this can create a large number of Events, which can fill up your log.       Step 8: Next Steps   Congratulations. You've completed the Manage an Engine Analytical Model guide. In this guide you learned how to:   Define an Analysis Provider that uses the built-in Analytics Server Connector Publish a Model from Analytics Builder to Manager Create an Analysis Event which takes data from ThingWorx Foundation and decides whether or not a failure is likely   The next guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path is Engine Failure-Prediction GUI.   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      
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    Step 7: Add Grid   It might also be helpful to display the data you've used to build the ThingWorx Analytics model.   In the future, this might be split out into an entirely separate page/Mashup that is exclusively devoted to backend model creation, but that would be beyond the scope of this guide.   For now, we'll simply display that collected data via the Grid Widget      1. On the EEFV_Mashup, drag-and-drop a Grid Advanced Widget onto the bottom-left Canvas section..     2. On the top-right Data tab, click the green </> button beside Things_EdgeThing. Note that this will open the Add Data pop-up, but with EdgeThing pre-selected.   3. In the Services Filter field, type getproperties.   4. Click the right-arrow beside GetProperties to add it to Selected Services on the right.   5. Check Execute on Load.     6. Click Done.   7. Under the Data tab, expand GetProperties to reveal the options.     8. Drag-and-drop Things_EdgeThing > GetProperties > infoTableProperty onto the Grid Advanced Widget.     9. On the Select Binding Target pop-up, click Data.     10. Click Save.   11. Click View Mashup.         Step 8: Add Controls   Throughout this Learning Path, it has been recommended that you stop Analysis Events when not actively using their functionality.   However, this requires going into the backend of ThingWorx Analytics to disable the event, which is not ideal.   Instead, let's enable or disable the Analysis Event from inside the Mashup by adding some Button Widgets to directly interface the Analytics backend for us.       1. Click the bottom-right Canvas section to select it.         2. In Mashup Builder top-left, click the Layout tab.         3. Under Positioning, click the Static radio-button.         4. Click the Widgets tab.       5. Drag-and-drop a Button Widget onto the bottom-right section.         6. Drag-and-drop another Button Widget onto the bottom-right section.       7. Drag-and-drop a Text Field Widget onto the bottom-right section.       8. Click Save.       Bring in More Data   Now that we have Buttons to trigger enable/disable, as well as a Text Field to display information, we now need to bring in some additional Mashup Data Services to interact with the ThingWorx Analytics backend.       1. Click the green + button at the top of the Data tab.       2. In the Entity Filter field, search for and  select TW.AnalysisServices.EventManagementServicesAPI.       3. In the Services Filter field, search for and add QueryAnalysisEvents by clicking the right arrow.       4. Check Execute on Load.         5. In Services Filter, search for and select EnableAnalysisEvent by clicking the right arrow. Note that you should NOT check "Execute on Load", as we'll trigger this Service only when the Button is clicked.     6. In Services Filter, search for and select DisableAnalysisEvent by clicking the right arrow. Likewise, do NOT check "Execute on Load" here either.       7. Click Done.         8. Click Save.     Display Event Key   To enable or disable Analytics Events, we need to know the eventId, which is returned by QueryAnalysisEvent Service as the parameter labeled key.   We'll bind that to the Text Field Widget for later usage in enabling/disabling.        1. Change the top-button's Label Property to Enable Analytics Event.       2. Change the bottom-button's Label Property to Disable Analytics Event.         3. Under the Data tab, expand QueryAnalysisEvents > Returned Data > All Data to reveal the options. .       4. Drag-and-drop QueryAnalysisEvents > Returned Data > All Data > key to the TextField Widget.         5. On the Select Binding Target pop-up, click Text.         6. Click Save.     Enable/Disable Analytics Events   Now that we know the key/eventId, we can call the EnableAnalysisEvent and DisableAnalysisEvent services.       1. Under the Data tab, expand EnableAnalysisEvent > Parameters to reveal eventId.       2. Click the Text Field Widget to select it, and then click the top-left drop down to reveal the options.         3. Drag-and-drop the Text Field's Text Property onto EnableAnalysisEvent > Parameters > eventId.         4. Repeat steps 1-3 for DisableAnalysisEvent.         5. Click the Enable Analytics Event Button Widget to select it, then click the top-left to reveal the drop down option .       6. Drag-and-drop the Clicked Event onto the EnableAnalysisEvent Service under the Data tab.         7. Repeat steps 5-6 for DisableAnalysisEvent, using the other Button Widget.         8. Click Save     Step 9: View Mashup   Throughout this guide, we've added various additional functionality to our MVP Mashup. At this point, you could continue to update the Mashup as you see fit.   For instance, you could change the background color of the top-left section to better match the header. Or you could further modify the original Mashup shown in the Contained Mashup Widget so that it better fits in the allowed space. You could add another Label Widget to the Header section to also display the company's motto / tag-line.   Regardless, when you are done with modifications, Save and click View Mashup.     Note that you can left-click-and-drag on the Time Series Chart to select particular time ranges. Or you could add a Time Selector Widget to the bottom-right section to control it there.   Similarly, you could add controls for the Grid Widget to only show the Identifier ranges in which you were interested.   Or you could split out the Model-creation values to a completely separate Mashup as previously discussed.   The extent to which you develop your Mashup is entirely up to you.        Step 10: Next Steps   Congratulations. You've completed the Enhanced Engine Failure Visualization guide. In this guide, you learned 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 Line Chart Show spreadsheet data via a Grid Advanced Widget Tie Mashup controls into the ThingWorx backend   This is the last guide in the Vehicle Predictive Pre-Failure Detection with ThingWorx Platform learning path.   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
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  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.  
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  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.
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    Generate engine-failure predictions and gain insight into your data with machine learning.   GUIDE CONCEPT   This guide will upload captured data from an Edge MicroServer (EMS) "Engine Simulator" to 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 parts of this guide is 60 minutes     YOU'LL LEARN HOW TO   Load an IoT dataset Generate machine learning predictions Evaluate the analytics output to gain insight     Step 1: Scenario   In this guide, we’re continuing the same 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).   The goal of this guide is to now import that previously-exported Comma-Separated Values (.csv) data into ThingWorx Analytics, and then create an analytical model for predictive maintenance.   Analytical model creation can be extremely helpful for the automotive 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 build an analytic model of your engine to facilitate this monitoring service.     Step 2: Upload Simulated Data   This guide assumes that you are using either the hosted trial (with has both Foundation and Analytics pre-installed) or a combination of the Foundation and Analytics downloadable installers.   To confirm that Foundation is communicating with Analytics, perform the following steps:   On the ThingWorx Foundation left-side navigation column, click Analytics > Analytics Builder > Settings.   At the top-right in the Analytics Server Version field, ensure that you see an appropriate version number.     NOTE:  If you use your own dataset, it's possible that you're results in the following steps will differ from those created by the provided-dataset. If you were unable to generate a 30,000+ entry dataset in the last guide, then you may download testCSVfile.csv attached here,instead. You will also need to download and extract vibration_metadata.zip which describes each column of the dataset. On the left, click Analytics Builder > Data.   Under Datasets, click New....   In the Dataset Name field, enter simulated_dataset. In the File Containing Dataset Data section, search for and select testCSVfile.csv. In the File Containing Dataset Field Configuration section, search for and select vibration_metadata.json.   Click Submit. Note that the time it takes to import the dataset is determined by its size.       Step 3: Simulated Signals and Profiles    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….   In the Signal Name field, enter simulated_signal. In the Dataset field, select simulated_dataset.   Click Submit. Wait ~30 seconds for Signal State to change to COMPLETED     Unfortunately, our results aren't very good. Or, more accurately, they're too good.   Our simulated dataset has some noise in it from adding random values to our five frequency bands on each our two sensors. However, ThingWorx Analytics has instantly seen through that noise and discarded it. Instead, it's only detected that s2_fb5 isn't relevant.   If you look back at the Use the EMS to Create an Engine Simulator guide, you'll see that s2_fb5 has the same base value between both a "good grease" and a "bad grease" condition, i.e. a base of 190.   This does show already that Analytics is working, though. Since s2_fb5 didn't change between good and bad grease conditions, our Signal analysis is indicating that it's not relevant to our model.   Profiles   Now, let's do the same for a Profile.   The Profiles section of ThingWorx Analytics looks for combinations of data which are highly correlated with your desired goal.   On the left, click Analytics Builder > Profiles.   Click New....   In the Profile Name field, enter simulated_profile. In the Dataset field, select simulated_dataset.   Click Submit. Wait ~30 seconds for the Profile State to change to COMPLETED.     Just like with Signals, our Profile is too good. In fact, Analytics is indicating that just s1_fb2 by itself is the primary indicator of good vs. bad grease conditions.   This is likely due to random chance. The random noise added to s1_fb2 just happened to be slightly less than the other frequency bands, so everything else was discarded.   Regardless, ThingWorx Analytics is quickly seeing through our simulated data.   Next, we'll actually create a Model using the simulated dataset.     Click here to view Part 2 of this guide  
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Build a Predictive Analytics Model Guide Part 2   Step 5: Profiles   The Profiles section of ThingWorx Analytics looks for combinations of data which are highly correlated with your desired goal. On the left, click ANALYTICS BUILDER > Profiles. Click New....The New Profile pop-up will open. NOTE: Notice the Text Data Only section which is new in ThingWorx 9.3.         3. In the Profile Name field, enter vibration_profile. 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 Profile field set to the default of empty. 8. Click Submit. 9. After ~30 seconds, the Signal State will change to COMPLETED. The results will be displayed at the bottom.                 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 combation with Sensor 1), then Sensor 2 may not be necessary.     Step 6: Create Model   Models are primarily used by Analytics Manager (which is beyond the scope of this guide), but they can still be used to measure the accuracy of predictions. When Models are calculated, they inherently withhold a certain amount of data. The prediction model is then run against the withheld data. This provides a form of "accuracy measure", which we'll use to determine whether Sensor 2 is necessary to the detection of a low grease condition by creating two different Models. The first Model (which you will create below) will contain all the data, while the second Model (in the next step) will exclude Sensor 2. On the left, click ANALYTICS BUILDER > Models.   Click New….The New Predictive Model pop-up will open.   3. In the Model Name field, enter vibration_model. 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 Model section at its default of empty.       8. Click Submit. 9. After ~60 seconds, the Model Status will change to COMPLETED.   View Model   Now that the prediction model is COMPLETED, you can view the results. Select the model that was created in the previous step, i.e. vibration_model. Click View… to open the Model Information page.   Review the visualization of the validation results. Note that your results may differ slightly from the picture, as the automatically-withheld "test" portion of the dataset is randomly chosen. Click on the ? icon to the right of the chart for details on the information displayed.   The desired outcome is for the model to have a relatively high level of accuracy. 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 that 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.     Step 7: Refine Model   We will now try comparing this first Model that includes both Sensors to a simpler Model using only Sensor 1. We do this because 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. Leave the Goal field set to the default of low_grease. Leave the Filter field set to the default of all_data.   On the right beside Excluded Fields from Model, click the Excluded Fields button. The Fields To Be Excluded From Job pop-up will open. 8. Click s2_fb1 to select the first Sensor 2 Frequency Band. 9. Select the rest of the Frequency Bands through s2_fb5 to choose all of the Sensor 2 frequencies. 10. While all the s2 values are selected, click the green "right arrow", i.e. the > button in the middle. 11. At the bottom-left, click Save. The Fields To Be Excluded From Job pop-up will close.           12. Click Submit. 13. After ~60 seconds, the Model State will change to COMPLETED. 14. 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 a Predictive Analytics Model guide, and learned how to:   Load an IoT dataset Generate machine learning predictions Evaluate the analytics output to gain insight    This is the last guide in the Getting Started on the ThingWorx Platform learning path.   This is the last guide in the Monitor Factory Supplies and Consumables learning path.   The next guide in the Design and Implement Data Models to Enable Predictive Analytics learning path is Operationalize an Analytics Model.     Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Support Analytics Builder Help Center    
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  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
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  Use the Statistical Calculation Thing Shape to Execute Common Statistical Functions   GUIDE CONCEPT   This project will introduce the Statistical Calculation Thing Shape.   The steps in this guide outline how to utilize Descriptive Analytics in ThingWorx Analytics to perform common mathematical analyses on data sets. You will learn how to use the Statistical Calculation Thing Shape's built-in functionality to calculate the mean, median, mode, and other useful insights.     YOU'LL LEARN 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 to perform the Mean, Median, Mode, and Standard Deviation calculations   NOTE: The estimated time to complete this guide is 30 minutes.     Step 1: Introduction   Descriptive Analytics enables users to perform on-demand common statistical calculations and enable statistical monitoring. Output from these Services can then easily be added to IoT applications built with the ThingWorx platform.   For example, output generated by Descriptive Analytics can be used to build solution-specific visualizations (Mashups or Widgets), create Alerts based on logged Property values, or generate transformed data for machine learning processes.   Descriptive Analytics includes two microservers, each with its own set of statistical Services. This guide will deal specifically with the Statistical Calculation Thing Shape.   This Thing Shape will add a variety of built-in Services to any Thing or Thing Template you choose, such as calculations for Mean, Median, Mode, or Standard Deviation.   To perform these statistical calculations, a Property must have time-series data logged to a Value Stream.   In addition, the Statistical Calculation Thing Shape has been optimized to perform calculations only on particular time ranges and with a maximum entry-count. This helps minimize some of the performance hits that can occur from repeatedly accessing Value Streams.   You perform this "data grooming" via the QueryTimedValuesForProperty Service, which is also part of the Statistical Calculation Thing Shape.   In addition to the name of a Property with values logged to a Value Stream, you also provide a Start Date, End Date, and Max Item Count.   The QueryTimedValuesForProperty Service then formats the values from the Value Stream to work with for the other built-in statistical calculation Services.   Step 2: Create Prerequisites   The inputs to the built-in Services of the Statistical Calculation Thing Shape require time-series data stored in a Value Stream. Setting a Thing's Properties to be Logged will store valid time-series data on which to perform Descriptive Analytics.   Create Value Stream   Follow the steps below to create a Value Stream that you will later tie to a Thing.   On the ThingWorx Composer Browse tab, click DATA STORAGE > Value Streams, + New.   Select ValueStream and click OK.   In the Name field, enter scts_valuestream. If Project is not already set, search for and select PTCDefaultProject.   At the top, click Save.   Create Data Shape   You will need a Data Shape to format the timed_values Property we will create in the next step.   On the ThingWorx Composer Browse tab, click MODELING > Data Shapes, + New.   In the Name field, enter scts_timed_values_datashape. If Project is not already set, search for and select PTCDefaultProject.   At the top, click Field Definitions.   Click + Add. On the right slide-out, in the Name field, enter value. Change the Base Type to NUMBER.   At the top-right, click the "check with a +" icon for Done and Add. In the Name field, enter timestamp. Change the Base Type to LONG.   At the top-right, click the "check" icon for Done. At the top, click Save.       Step 3: Create Thing   Next, you will create a Thing and tie the previously-created Value Stream to it.   You will assign the Statistical Calculation Thing Shape to get a variety of built-in analytics Services to manipulate the data. You will also create a series of Properties to facilitate the new Services.   On the ThingWorx Composer Browse tab, click MODELING > Things, + New.   In the Name field, enter scts_thing. If Project is not already set, search for and select PTCDefaultProject. In the Base Thing Template field, search for and select GenericThing. In the Implemented Shapes field, search for and select StatisticalCalculationThingShape. In the Value Stream field, search for and select scts_valuestream.   At the top, click Save.   Add Properties   Next, you will add a series of Properties to scts_thing.   Perform the following steps repeatedly until all Properties have been added.   At the top, click Properties and Alerts.   Click + Add. On the right slide-out, in the Name field, enter numbers. Change the Base Type to NUMBER. Click Persistent. Click Logged.   At the top-right, click the "check with a +" icon for Done and Add. Repeat steps 3-7 until all of the properties in the table below have been added. NOTE: For the final standarddev_result Property, do NOT click Done and Add; you'll just click the check for Done instead. Property Name Base Type Data Shape Persistent  Logged start_time DATETIME N/A Checked NOT checked end_time DATETIME N/A Checked NOT checked timed_values INFOTABLE scts_timed_values_datashape Checked NOT checked mean_result NUMBER N/A Checked NOT checked median_result NUMBER N/A Checked NOT checked mode_result INFOTABLE none, i.e. leave it blank Checked NOT checked standarddev_result NUMBER N/A Checked NOT checked     At the top-right, click the "check" icon for DONE. At the top, click Save.   Step 4: Set Properties   You will now set the properties to guarantee that each calculation gives us a different answer.   Mean is the average. Median is the "middle" number from the dataset. Mode is the most common number. Standard Deviation is a measure of the "dispersion" of the dataset.   You will use the following dataset: 1, 5, 9, 5, 9, 1, 9.   The mean is 39 / 7 = 5.571...   The median is 5, as 5 is the middle of 1, 5, and 9.   Mode is 9, because 9 appears most commonly in the dataset.   Standard Deviation is 3.5989...   Perform the following steps to enter the above values into the numbers property.   Set numbers   Under the Value column and on the numbers property row, click the "pencil" icon for Set value of property.   On the right slide-out, enter 1.   At the top-right, click the "check" icon for Done. At the top, click Save.   Repeat steps 1-4 above, changing the value each time according to the table below: Value Change Count  Entered Value 2nd 5 3rd 9 4th 5 5th 9 6th 1 7th 9   You also need to set the start_time and end_time. These are dates used to define the time-period in which the Statistical Calculation Thing Shape will search for values.   For instance, you could set the calculations to run at midnight, and then use the past 24-hours as your time-period.   For this example, set dates to be 24 hours prior to the time at which you set the above values of the numbers property, through 24 hours in the future.   Set start_time   Under the Value column and on the start_time Property row, click the "pencil" icon for Set value of property.   On the right slide-out, search for and select the previous day.   At the top-right, click the "check" icon for Done. At the top, click Save.   Set end_time   Under the Value column and on the end_time property row, click the "pencil" icon for Set value of property.   On the right slide-out, search for and select the following day. At the top-right, click the "check" icon for Done. At the top, click Save.     Click here to view Part 2 of this guide.
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  Use the Edge MicroServer (EMS) to simulate an engine with vibration sensors.   GUIDE CONCEPT   The Edge MicroServer (EMS) facilitates connectivity from Edge devices to ThingWorx Foundation.   It’s often easier, though, to start development with simulated Edge values rather than hooking up sensors.   This guide will show you how to simulate vibration values of an engine using the EMS.     YOU'LL LEARN 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   NOTE:  The estimated time to complete all parts of this guide is 30 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.   In this guide, you’ll begin this monitoring process by using ThingWorx Foundation to monitor and record vibration data from the prototype motor. In particular, you will learn how to provision Thing Properties and Values from an EMS, as well as how to permanently store these values for analysis in an Info Table Property.   These types of modifications to an EMS can be extremely helpful for the automotive segment in particular. For instance, each car that comes off the factory line could have custom, auto-generated EMS scripting that would dynamically create Foundation information for each car in the fleet. This could be a massive time-savings versus manually generating Thing Properties directly within Foundation.   Because the motor is still in the process of being instrumented with sensors, you’ll get all the functionality in-place beforehand by constructing a motor simulator using the Edge MicroServer (EMS).     Step 2: Modify config.lua   In the previous Use the Edge MicroServer (EMS) to Connect to ThingWorx  guide, you installed the EMS on a Windows PC, configured it to talk to ThingWorx Foundation, and then created an EdgeThing on Foundation to complete the connectivity.   This guide assumes that you have already completed that Windows EMS guide and have an active EMS connection to the EdgeThing.   Perform the following steps to modify this connection to increase the task rate of the EMS, which we'll use in the following steps to update Properties more quickly.   On your Windows PC, select the Windows PowerShell window where the luaScriptResource.exe program is running.   Type Ctrl-C to close the luaScriptResource.exe operation, i.e. hold the Control key and hit the C key.   Minimize the luaScriptResource.exe PowerShell window, and activate the wsemse.exe PowerShell window.   Type Ctrl-C to close the wsems.exe operation.   Return to Foundation, and note that EdgeThing is not connected.   Navigate to the C:\CDEMO_EMSE\etc directory.   Open config.lua in your prefered text-editor.   Change scanRate to 1000. Add the following line below the scanRate line: taskRate = 1000,   The final code of config.lua should be the following Note that the EMS may have slightly modified your config.lua file, such as adding a data_security_key line. Leave these EMS-generated modifications alone. scripts.log_level = "WARN" scripts.script_resource_ssl = false scripts.script_resource_authenticate = false scripts.EdgeThing = { file = "thing.lua", template = "YourEdgeThingTemplate", scanRate = 1000, taskRate = 1000, sw_update_dir = "C:\\CDEMO_EMS\\updates" } Save the config.lua file.     Click here to view Part 2 of this guide.
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    Step 3: Modify YourEdgeThingTemplate.lua   Now that the task rate has been decreased to 1000ms (one second), we can create a series of Thing Properties.   In Windows, navigate to C:\CDEMO_EMS\etc\custom\templates.   In your prefered text-editor, open YourEdgeThingTemplate.lua.   We now want to add several lines of Lua code to define some Properties for EdgeThing. You’ll do some with some references that are pre-built into the EMS, primarily the properties structure.   Working with the engine R&D team, their plan is to place two vibration sensors on the proptype engine. In addition, each vibration sensor will have five frequency bands. As such, we’ll need ten Properties to represent the vibration readings.   In addition, we also want a Property that will record whether or not the engine is currently experiencing the low grease condition. This will be entered via manual-inspection at the same time that the frequency readings are recorded.   Perform the following to implement the ten vibration frequency bands and the low grease condition.   Below the module line of YourEdgeThingTemplate.lua, add the following line to create a low_grease Property: properties.low_grease={baseType="NUMBER", pushType="ALWAYS", value=0} Below that, add the following lines to create the five frequency bands for the first vibration sensor: properties.s1_fb1={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb2={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb3={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb4={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb5={baseType="NUMBER", pushType="ALWAYS", value=0} Below that, add the following lines to create the five frequency bands for the second vibration sensor: properties.s2_fb1={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb2={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb3={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb4={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb5={baseType="NUMBER", pushType="ALWAYS", value=0} Your code should now look like the picture below.   The code above adds each new Property to the properties structure, and the name of the Property will be what follows after the “.”, i.e. low_grease, s1_fb1, s1_fb2, etc.   In addition, the baseType defines the type of each Property, in this case, all Numbers.   The pushType of ALWAYS means that there are no restrictions on sending new Property values up to Foundation, and the value of 0 indicates the default value to which each Property will initially be set.   Generate Property Values   Now that we have the Properties defined, we want to add code which will give us different values.   To do so, we’ll define a queryHardware function, and tie the calling of it to the task rate which we had set earlier. This queryHardware function will use random numbers to simulate code that would gather actual data.    Add the following Lua code to define a GetSystemProperties function. Note that this calls a separate queryHardware function which we split out to also be called by the tasks timer. serviceDefinitions.GetSystemProperties( output { baseType="BOOLEAN", description="" }, description { "updates properties" } ) services.GetSystemProperties = function(me, headers, query, data) queryHardware() return 200, true end Add the following Lua code to define queryHardware. Note that Lua’s random number generation requires a new seed on each calling, and the randomseed function is using the built-in os.time function (plus some additional noise created by turning that time into a string and back). function queryHardware() math.randomseed( tonumber(tostring(os.time()):reverse():sub(1,6)) ) local temp = math.random(10) if temp < 6 then properties.low_grease.value=0 properties.s1_fb1.value=161+math.random() properties.s1_fb2.value=180+math.random() properties.s1_fb3.value=190+math.random() properties.s1_fb4.value=176+math.random() properties.s1_fb5.value=193+math.random() properties.s2_fb1.value=130+math.random() properties.s2_fb2.value=200+math.random() properties.s2_fb3.value=195+math.random() properties.s2_fb4.value=165+math.random() properties.s2_fb5.value=190+math.random() else properties.low_grease.value=1 properties.s1_fb1.value=90+math.random() properties.s1_fb2.value=170+math.random() properties.s1_fb3.value=170+math.random() properties.s1_fb4.value=95+math.random() properties.s1_fb5.value=190+math.random() properties.s2_fb1.value=165+math.random() properties.s2_fb2.value=195+math.random() properties.s2_fb3.value=190+math.random() properties.s2_fb4.value=140+math.random() properties.s2_fb5.value=190+math.random() end end Finally, we want to tie the calling of queryHardware to the tasks timer by adding the following code: tasks.refreshProperties = function(me) queryHardware() end   We now have code in our EMS template that not only defines the low grease condition and the five frequency bands of our two vibration sensors, but also generates some values in the ranges that R&D have typically seen in both good grease amount and bad grease amount conditions.   The final Lua code of the YourEdgeThingTemplate.lua file should look like the following:   require "shapes.swupdate" module ("templates.YourEdgeThingTemplate", thingworx.template.extend) properties.low_grease={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb1={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb2={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb3={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb4={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s1_fb5={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb1={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb2={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb3={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb4={baseType="NUMBER", pushType="ALWAYS", value=0} properties.s2_fb5={baseType="NUMBER", pushType="ALWAYS", value=0} serviceDefinitions.GetSystemProperties( output { baseType="BOOLEAN", description="" }, description { "updates properties" } ) services.GetSystemProperties = function(me, headers, query, data) queryHardware() return 200, true end function queryHardware() math.randomseed( tonumber(tostring(os.time()):reverse():sub(1,6)) ) local temp = math.random(10) if temp < 6 then properties.low_grease.value=0 properties.s1_fb1.value=161+math.random() properties.s1_fb2.value=180+math.random() properties.s1_fb3.value=190+math.random() properties.s1_fb4.value=176+math.random() properties.s1_fb5.value=193+math.random() properties.s2_fb1.value=130+math.random() properties.s2_fb2.value=200+math.random() properties.s2_fb3.value=195+math.random() properties.s2_fb4.value=165+math.random() properties.s2_fb5.value=190+math.random() else properties.low_grease.value=1 properties.s1_fb1.value=90+math.random() properties.s1_fb2.value=170+math.random() properties.s1_fb3.value=170+math.random() properties.s1_fb4.value=95+math.random() properties.s1_fb5.value=190+math.random() properties.s2_fb1.value=165+math.random() properties.s2_fb2.value=195+math.random() properties.s2_fb3.value=190+math.random() properties.s2_fb4.value=140+math.random() properties.s2_fb5.value=190+math.random() end end tasks.refreshProperties = function(me) queryHardware() end       Step 4: Modify EdgeThing   Now that our EMS has been updated with Properties, as well as code to generate values for those Properties, we want to re-connect the EMS to Foundation and update the EdgeThing.   Note once again that EdgeThing was previously created in the Use the Edge MicroServer (EMS) to Connect to ThingWorx guide.   Restart the wsemse.exe program by returning to its PowerShell window and executing the following command: .\wsems.exe   Restart the luaScriptResource.exe program by returning to its separate PowerShell window and executing the following command: .\luaScriptResource.exe   Return to ThingWorx Foundation's EdgeThing. Note that EdgeThing is connected.   On the Properties and Alerts tab, click Manage Bindings.   At the bottom-left of the Manage Bindings pop-up, click + Add all properties.   At the bottom-right of the pop-up, click Done.   At the top, click Save.   Near the top, click Refresh repeatedly. Note that the Property values consistently change.          Click here to view Part 3 of this guide.
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  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.
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  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  
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    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.
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Welcome to the ThingWorx Analytics Training Course! Through these 11 modules, you will learn all about the functionality of this software, as well as techniques to help you build a successful and meaningful predictive analytics application.
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This video is Module 10: ThingWorx Foundation & Analytics Integration of the ThingWorx Analytics Training videos. It gives a brief review of core ThingWorx Platform functionality, and how the Analytics server works on top of the platform. It also describes the process of creating a simple application, complete with a mashup to display the information from a predictive model.
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