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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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    Use ThingWorx Kepware Server as an OPC UA Client   GUIDE CONCEPT   This guide will explain how ThingWorx Kepware server can function as both an OPC UA Server, and a client to a remote OPC UA Server.   Following the steps in this guide, you will create a OPC UA server in Azure, then we will teach you how to use data from the OPC UA server in ThingWorx.       YOU'LL LEARN HOW TO   Create an OPC UA Server in Azure Configure Kepware as on OPC UA Client Connect Kepware to ThingWorx Foundation Monitor OPC UA data in ThingWorx Composer   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 60 minutes     Step 1: Overview Diagram   In this guide, ThingWorx Kepware Server will serve as both an OPC UA client, and a ThingWorx Foundation client. ThingWorx Kepware Server is able to connect through firewalls to provide a seamless, end-to-end connection from an OPC UA server to ThingWorx Composer. Two ThingWorx Kepware Server instances can be configured to provide a tunnel for transporting machine data across the internet.       This guide will show how to create an OPC UA server in Azure, then browse the server data using Kepware. We will create a ThingWorx Thing with a Property that dynamically represents the value on the remote server.     Step 2: Install ThingWorx Kepware Server   In addition to OPC UA, ThingWorx Kepware Server includes over 150 factory-automation protocols. ThingWorx Kepware Server communicates between industrial assets and ThingWorx Foundation, providing streamlined, real-time access to OT and IT data — whether that data is sourced from on-premise web servers, off-premise cloud applications, or at the edge. This step will download and install ThingWorx Kepware Server. Download the ThingWorx Kepware Server executable installer. Right-click on the installer and select Run as administrator. Click Yes in the pop-up asking if you want to proceed. Select your Language and click OK.     On the "Welcome" screen, click Next.     Accept the End-User License Agreement and click Next.     Set the destination folder for the installation and click Next.     Set the Application Data Folder location and click Next. Note that it is recommended NOT to change this path.     Select whether or not you'd like a Shortcut to be created and click Next.     On the "Vertical Suite Selection" screen, keep the default of Typical and click Next.     On the "Select Features" screen, keep the defaults and click Next.     The "External Dependencies" screen simply lists everything that will be installed; click Next.     On the "Default Application Settings" screen, leave the default of Allow client applications to request data through Dynamic Tag address and click Next.     On the “User Manager Credentials” screen, set a unique strong password for the Administrator account and click Next. Note that skipping setting a password can leave your system less secure and is not recommended in a production environment.     Click Install to being the installation.     Click Finish to exit the installer.       Step 3: Create Industrial Gateway   To make a connection between ThingWorx Kepware Server and ThingWorx Foundation, you must first create a Thing.    In ThingWorx Composer, click Browse. On the left, click Modeling -> Things.     Click + NEW. In the Name field, enter IndConn_Server, including matching capitalization.     If Project is not already set, click the + in the Project text box and select the PTCDefaultProject. In the Base Thing Template field, enter indus, then select the IndustrialGateway Thing template from the sorted list. Click Save.   Step 4: Connect Kepware to ThingWorx   This step will get ThingWorx Kepware Server set-up and connected to ThingWorx Foundation.   Now that you have created an Industrial Gateway Thing, you can configure ThingWorx Kepware Server to connect to ThingWorx Foundation.   Follow the steps to Create an Application Key and note the value. The appKey will be used in the the next step. Open the ThingWorx Kepware Server Configuration Windows application, then right-click on Project.     Select Properties….     In the Property Editor pop-up, click ThingWorx. In the Enable field, select Yes from the drop-down. In the Host field, enter the URL or IP address of your ThingWorx Foundation server, without http:// or https://. Enter the Port number.       In the Application Key field, copy and paste the Application Key you just created. In the Trust self-signed certificates field, select Yes from the drop-down. In the Trust all certificates field, select Yes from the drop-down. In the Disable encryption field, select No from the drop-down if you are using another server that uses TLS - URL begins with https://. Select Yes if you are using a ThingWorx Foundation server without TLS - URL begins with http:// Type IndConn_Server in the Thing Name field, including matching capitalization. If you are connecting with a remote instance of ThingWorx Foundation and you expect any breaks or latency in your connection, enable Store and Forward. Click Apply in the pop-up. Click Ok.   In the ThingWorx Kepware Server Event window at the bottom, you should see a message indicating Connected to ThingWorx.     NOTE: If you do not see the “Connected” message, repeat the steps above, ensuring that all information is correct. In particular, check the Host, Port, and Thing name fields for errors.     Step 5: Connect OPC UA Server to Kepware   Now that you have created an Industrial Gateway Thing, and ThingWorx Kepware Server is connected to ThingWorx Foundation, we can connect an OPC UA server to Kepware   Follow the steps to create an OPC UA server in Azure. Enter Resource Group, click Review + create.     Click Create.     In about a minute, the deployment success screen will be displayed.     Click Go to resource, and copy FQDN.     Open ThingWorx Kepware Server Configuration.     Right-click, then click New Channel. Scroll down to select OPC UA Client, then click Next.     Click Next twice to accept default settings, then enter the FQDN copied earlier. Add :50000 to the end of the domain name.     Click Next to accept all defaults. Click Yes to trust the certificate.   Click here to view Part 2 of this guide.
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  Convey information about IoT data effectively by customizing style definitions and implementing event-based logic   Guide Concept   This project will help you identify how you would like to create an experience for Users.   Following the steps in this guide, you will use color schemes to convey information quickly and effectively, for example to alert users of critical events. With ThingWorx Composer, you can implement Styles and States in your Mashups to enhance your user experience.   We will teach you how to create an affective IoT application experience that looks great and easy to navigate. How the UI is presented can influence users and their enjoyment of the application.   You'll learn how to   Create a Style Definition Customize Style Definitions Create and implement State Definitions Implement event-based state changes   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 60 minutes       Step 1: Completed Example   Download the StylesAndStates.xml attached to this guide.  Within this file, you will find Entities referenced in this lesson, including a finished application.  Import and utilize this file to see a finished example and return to it as a reference if you become stuck during this guide and need some extra help or clarification.   Keep in mind, this download uses the exact names for entities used in this tutorial. If you would like to import this example and also create entities on your own, change the names of the entities you create.     Step 2: Create Style Definition   A Style Definition is a collection of HTML styling elements that can be applied to a Widget just as you would apply a CSS definition to an HTML tag. With Style Definitions, you can control the look and feel, such as colors, fonts, and color context of individual Widgets in your Mashup.   In the ThingWorx Composer, click the + New at the top of the screen.   Select Style Definition in the dropdown.   Enter a name for the Style Definition, such as StyleDefinition. Set the Project to an existing Project (ie, PTCDefaultProject).   Click Style Information.   The Style Information page shows the options for images, colors, lines, and display text. See the table below for information on what each field controls.   6. Type PlaygroundBackground in the Display String field.   NOTE: If you go back to the HelloWorldPlayground, clear the Mashup Style property, then search for StyleDefinition again, you will see the PlaygroundBackground descriptive text.   7. Select Background Color. A color pallet will appear. Select White and click Select.   8. Select Text Color. A color pallet will appear. Select Black and click Select.   9. Click Save.     You have now created your first Style Definition. To ensure a consistent user experience, we recommend creating a Style Definition that you can use throughout your application.    Option                                    Description Display String Descriptive string that can be displayed to indicate the current applied style definition Background Color Background for charts, buttons, panels, etc Secondary Background Color Meant for widgets that support gradients Foreground Color Used for foreground characteristics such as button text and label text Font Bold For text, whether the text should be bold or not Font Italic For text, whether the text should be italicized or not Font Underline For text, whether the text should be underlined or not Image Add images Line Color Pen styling in charts Line Thickness Pen styling in charts Line Style Generally refers to borders. ThingWorx provides the following options: Solid, Dashed, Dotted, None Text Size Choose a font size from 9-72px   In the next part of this exercise, you’ll learn how to use Style Definitions to create an engaging experience for your application users.       Step 3: Customize Style Definitions   Open the HelloWorldPlayground Mashup in Composer, and click View Mashup.   It shows a Button that sends an Event to a Gauge Widget, which then updates a Line Chart.     Modify Style Definition   In this part of the lesson, we'll make some changes to this Mashup. We will use Style Definitions to change the background of the Mashup, change the colors used in the Line Chart in order information stand out, and add color to the Gauge Widget.   In the Explorer tab, select the Mashup. Select the Style Properties tab, then click the X button to clear the Style Mashup Properties.   When editing a Mashup, you can either use a Style Definition Thing that you created earlier OR you can click the wand in a style property for a Mashup or Widget followed by clicking the + Custom button to create a one-time-use style. 3. With the Style Property clear, enter the Style Definition you created in the last section. Update the Background Color to #FF9082 to have the color pop in the Mashup.   4. Click Save and View Mashup to see the changes. You have now updated the background for the HelloWorldPlayground. The style properties you define in the Style Definition will be consitent for any Mashup that references this Style Definition. Change the style around or create a custom style and see the changes in the Mashup. Below is what we'll be working to create. Get ideas of things you might want to see differently in your styling.     Customize Widget Style   ThingWorx provides a default Style Definition for many of its Widgets. Before editing the Style Definitions of a Widget, click the Style Definition property then click View. This enables you to see what the current values are and what you might want to change. If the changes are slight, create a copy of the original Style Definition and update the new version.   Until you are sure of the color schemes you would like to implement, use the default Style Definitions as a guide when creating your own versions.   Default Style Definitions   Next, we will update the colors and style of the Line Chart.   Open the HelloWorldPlayground and select the Line Chart in the Workspace pane. Click the Style Properties tab to see the chart styles section.   Update the Legend->Color property to Blue.     Customize Chart Style Theme   In this part of the lesson, we will update the Series1 and StyleTheme properties of the Line Chart. This is how you'll also set the colors for the chart titles.   The Series1 property will update the look and feel of the line for the count value being used. The Line Chart is a line graph, thus the only property you need to change is the Line Color property.   The StyleTheme property will update the background look of the Line Chart grid.   Clear the StyleTheme and click the + button to create a new theme. Create a theme with the name CustomTheme.   Click the Style tab and edit the feel of the items as you see fit.   After open the Style Theme to be editable, click on colors. Here you'll see all the options and fields that you can make up your own color options and be as conservative as you like or as free as you like.     Click Text Colors, then click on the Grids and Lists tab on the right. This is where we will be shaping our colors for the chart. When you're done with this, update the Core Colors section to make your mashup pop even more.     You may also notice a more focused method of updating grids and lists. In the below Elements section, you'll have a more focused experience for updated.     NOTE: As an extension, after completing the previous steps, try to use Style Definitions to customize the sections of the UI on your own.     Click here to view Part 2 of this guide.
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  Maintain cookies and security information by implementing session parameters in your application.   Guide Concept   This project will introduce creating and accessing session data from a User logged into your application. Session data is global session-specific parameters that can be used on the Client and Server side.   Following the steps in this guide, you will be able to access the logged in User's information and their set values.   We will teach you how to access session data, that can later be used to provide Users with unique experiences and a more robust application.   You'll learn how to   Create Session Data Access Stored Session Data   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 30 minutes     Step 1: Completed Example   Download the completed files for this tutorial:  Sessions.xml.   The Sessions.xml file contains a completed example of session parameters. Utilize this file to see a finished example and return to it as a reference if you become stuck during this guide. Keep in mind, this download uses the exact names for entities used in this tutorial. If you would like to import this example and also create entities on your own, change the names of the entities you create.     In the bottom-left of Composer, click Import/Export.     Click IMPORT.     In the Import pop-up, keep the default values and click Browse. Navigate to the Sessions.xml file you downloaded. Select it and click Open. Click Import in the Import pop-up. Click Close to close the pop-up.       Step 2: Create Session Parameters  Click the Browse folder on the left-hand side. Under System, select Subsystems.     Filter for UserManagementSubsystem and open it in Edit mode.     Select Services. Filter for the AddSessionShape Service.     Click the Play button to open the Execute window. Enter UserLogin (the provided ThingShape) as the name input field. Click Execute.     Click Done.   You've just created your first Session Parameter. These values are used for content held in a cookie for a website or information that might be static for the User or session.   Best Practice: For information that will be static for the entire application and not based on the session, use a database option or a stored value in a Thing.       Step 3: Access Session Parameters   Click the Browse folder on the left-hand side. Under System, select Resources.   Filter for CurrentSessionInfo and open it.   Select Services. Filter for the GetGlobalSessionValues Service.   Click the Play button to open the Execute window. Click Execute. You will notice the result is a list of the properties in the UserLogin ThingShape. Your result might differ from mine.   Click Done.   NOTE: There is a difference between Session parameters and Mashup parameters. Mashups can have input values that will be used for services or content of that Mashup ONLY. Session parameters are based on the user using the application in a session. This data will be accessible throughout the application and last until they have completed their usage. This guide shows how to create Session parameters that are considered global session parameters.     Step 4: Next Steps   Congratulations! You've successfully completed the Create Session Parameters guide, and learned how to: Access a logged-in user's information and their set values Use session data to provide users with unique experiences and a more robust application   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Create Custom Business Logic Build Data Model Introduction   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource  Link Community Developer Community Forum Support Session Parameter Help Center  
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    Send data from an MXChip Developer kit to your Azure IoT Hub   GUIDE CONCEPT   Users of the MXChip IoT DevKit (a.k.a. MXChip), follow these quick steps to send temperature and humidity data from built-in IoT DevKit sensors to the Azure IoT Hub.   YOU'LL LEARN HOW TO   Connect the IoT DevKit to a wireless access point Create an Azure IoT Hub and register a device for the IoT DevKit Connect IoT Devkit to Azure IoT Hub   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 80 minutes   Step 1: Create an Azure IoT Hub   Choose +Create a resource, then choose Internet of Things. Click Iot Hub from the list on the right. You see the first screen for creating an IoT hub.   Fill in the fields.   Subscription: Select the subscription to use for your IoT hub.   Resource Group: You can create a new resource group or use an existing one. To create a new one, click Create new and fill in the name you want to use. To use an existing resource group, click Use existing and select the resource group from the dropdown list.   Region: This is the region in which you want your hub to be located. Select the location closest to you from the dropdown list.   IoT Hub Name: Put in the name for your IoT Hub. This name must be globally unique. If the name you enter is available, a green check mark appears.         3. Click Next: Size and scale to continue creating your IoT hub.     On this screen, you can take the defaults and just click Review + create at the bottom.   Pricing and scale tier: You can choose from several tiers depending on how many features you want and how many messages you send through your solution per day. The free tier is intended for testing and evaluation. It allows 500 devices to be connected to the IoT hub and up to 8,000 messages per day. Each Azure subscription can create one IoT Hub in the free tier.   IoT Hub units: The number of messages allowed per unit per day depends on your hub’s pricing tier. For example, if you want the IoT hub to support ingress of 700,000 messages, you choose two S1 tier units.   Advanced / Device-to-cloud partitions: This property relates the device-to-cloud messages to the number of simultaneous readers of the messages. Most IoT hubs only need four partitions.               4. Click Review + create to review your choices. You see something similar to this screen.           5. Click Create to create your new IoT hub. Creating the hub takes a few minutes.     Step 2: Create IoT device   Navigate to the IoT Hub created and in the IoT Devices page, click + New.   2. Enter the device ID used by the demo MXChip application MyNodeDevice. Use the default settings for auto-generating authentication keys and connecting the new device to your hub. Click Save.   3. Navigate to the device created and make a note of the device connection string, which looks like: HostName={YourIoTHubName}.azure-devices.net;DeviceId=MyNodeDevice;SharedAccessKey={YourSharedAccessKey}.   Create Azure Storage   The ThingWorx Azure IoT Connector we will install in the next guide requires an Azure Storage Account. Follow the Microsoft documentation to create an Azure Storage account. NOTE: Select Blob storage as the account type and the Hot Access Tier.     Step 3: Connect to Azure IoT Hub   Download the latest version of GetStarted firmware for IoT DevKit. Connect IoT DevKit to your computer via USB. In Windows you see a new USB mass storage device in Windows Explorer called AZ3166. Drag and drop the .bin file you downloaded from step 1 into the disk named AZ3166 and wait for IoT Devkit to restart. Internet connectivity is required to connect to Azure IoT Hub. Use AP Mode on the DevKit to configure and connect to Wi-Fi.Hold down button B, push and release the Reset button, and then release button B. Your IoT DevKit enters AP mode for configuring the Wi-Fi connection. The screen displays the service set identifier (SSID) of the DevKit and the configuration portal IP address:     5. Use a Web browser on a different Wi-Fi enabled device (computer or mobile phone) to connect to the IoT DevKit SSID displayed in the previous step. If it asks for a password, leave it empty.     6. Open 192.168.0.1 in the browser. Select or input the Wi-Fi network that you want the IoT DevKit to connect to, type the password for the Wi-Fi conection and input the device connection string you made notge of in step 1. Then click Connect.     7. The WiFi credentials and device connection string will be saved in the IoT DevKit even after power cycliong. The following page will be displayed in the browser:     8. The IoT DevKit reboots in a few seconds. You then see the assigned Wi-Fi IP address on the screen of the IoT DevKit:     9. Wait for the IoT DevKit to connect to Azure IoT Hub and you will see it sending telemetry data including temperature and humidity value to Azure IoT Hub. The screen of the IoT Devkit would show message count and temperature/humidity data.       Step 4: Next Steps   Congratulations! You've successfully completed the Connect MXChip to Azure IoT guide. By following the steps in this lesson, you created an Azure IoT Hub and device.     The next guide in the Azure MXChip Development Kit learning path is Create an Application Key.   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Analyze Build a Predictive Analytics Model Build Get Started with ThingWorx for IoT   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Azure Support Page    
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    Use Thing Shapes to create groups of related Properties   Guide Concept   Save time and effort by modeling a solution in ThingWorx using Thing Shapes to group Properties. A logical group of Properties can be applied to Things and Thing Templates.     You'll learn how to   Create a Thing Shape Add Properties to a Thing Shape   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 30 minutes       Step 1: Create Thing Shape   In this section you will create a Thing Shape for sensor properties of a MXChip development board.   Thing Shapes are components that contain Properties and Services. In Java programming terms, they are similar to an interface.   Start on the Browse folder icon of ThingWorx Composer. Under the Modeling section of the left-hand navigation panel hover over Thing Shapes, then click the + button.   Type ThermostatShape in the Name field.   If Project is not already set, click the + in the Project text box and select the PTCDefaultProject. Click Save.     Step 2: Add Properties to Thing Shape   Click Properties and Alerts tab at the top of your Thing Shape.   Click + Add. Enter the Property name in the Name field as shown in the table below. Name Base     Type           Persistent?       Logged? humidity NUMBER   X messageId STRING X   temperature NUMBER   X Select the appropriate Base Type from the drop-down menu.   Check Persistent and/or Logged according to the table. NOTE: When Persistent is selected, the property value will be retained during a system restart. Properties that are not persisted will be reset to the default during a system restart. When Logged is selected, every property value change will be automatically logged to a specified Value Stream. Click Check +. TIP: When adding multiple properties at once, click Done and Add after each, once you've entered a Name, selected a Base Type and any other criteria. If adding a single property, click Done. Repeat steps 4 through 6 for each of the properties in the rows of the table. Click the done Check. You'll see that these Properties have been created for the ThermostatShape.   Click Save.     Step 3: Next Steps   Congratulations! You've successfully completed the Create A Thing Shape, and learned how to: Create a new Thing Shape Add Properties to the Thing Shape   This is the last guide in the Azure MXChip Development Kit Learning Path.  If you wish to return to the Learning Path, click the link.   Learn More   The following resources continue your learning experience:  Resource       Link Build Data Model Introduction Experience Object-Oriented UI Design Tips   Additional Resources   If you have questions, issues, or need additional information, refer to:  Resource       Link Community Developer Community Forum Support Thing Shape Support Help Center      
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  A series of training videos for ThingWorx Analytics   Guide Concept   This guide provides a series of training videos covering ThingWorx Analytics Server and Platform Analytics.   It is recommended that they be viewed in order.    Additionally, a downloadable .zip with additional training materials is provided.     You'll learn how to   Use ThingWorx Analytics Understand the Analytics Thought Process Basic Analytics concepts (data types, variables, modeling) Descriptive Analytics Predictive Analytics modeling techniques Familiarity with other topics: Prescriptive Analytics, Clustering, Time Series, Anomaly Detection Acquire basic knowledge of how to create an end-to-end Smart Application Deepen your Foundation and Analytics understanding NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete all parts of this guide is 12 hours       ThingWorx Analytics Overview   This guide will present a series of training videos for ThingWorx Analytics.It is recommended that you view each guide in-order, as future videos may build on the concepts learned in earlier ones.    Download CourseFiles.zip included in this guide, as it contains important materials for particular videos.   In this course, you will learn the basics of the ThingWorx Analytics Machine Learning process. You will understand how to perform Descriptive Analytics such as identifying Signals, Profiles, or building Clusters.   You will also understand how to train a Model and use Predictive and Prescriptive Scoring. Additional topics include Time Series, Anomaly Detection, and near-real-time Scoring. You will also learn about how to create a simple smart application using Analytics together with other pieces of the overall ThingWorx platform.     ThingWorx Analytics Video Guide   Module 1: ThingWorx Analytics Overview Module 2: Use Case Discussion Module 3: Data Profiling Module 4: Data Transformation and Feature Engineering Module 5: Descriptive Analytics Module 6: Predictive Models and Model Validation Module 7: Scoring Predictive Realtime Prescriptive Module 8: Time Series Modeling Module 9: Anomaly Detection Module 10: ThingWorx Foundation and Analytics Integration Module 11: Mini Project     Next Steps   Congratulations! You've successfully completed the Analytics Training Videos guide, and learned how to:   Use ThingWorx Analytics Understand the Analytics Thought Process Basic Analytics concepts (data types, variables, modeling) Descriptive Analytics Predictive Analytics modeling techniques Familiarity with other topics: Prescriptive Analytics, Clustering, Time Series, Anomaly Detection Acquire basic knowledge of how to create an end-to-end Smart Application Deepen your Foundation and Analytics understanding   Additional Resources If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Analytics Support Help Center          
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    Step 3: Creating Customer Data   Now let’s begin creating the customer data. Just enough examples for us to understand what is happening at each step. Let’s create at least 4 customers by following the steps below:   Open the Fizos.Customers.DataTable Data Table and go to the services tab. Open the AddDataTableEntries service to be executed. This service will allow us to create some general data to work with. You can create as many as you like for this test. Click the values parameter to start creating entries. After clicking + Add, and enter data for customers. Try to add at least 1 Factor data tag for each customer.     Save your entry and create a second entry with any location and tags you like. We aren’t adding vehicles as of yet, but we will in the next section. After saving, don’t forget to execute the service with the two entries saved. If you did it correctly, the values parameter of the service, should show at least 1 inside of the parentheses. See below for an example:     We just added customers manually. While convenient for our test, what we truly want is a system that is hands off. What we need is, a way to add customers programatically. Whether a customer is ordering on a website you created for them or they are checking out as a guest (we still want to track this). Below, you’ll see a quick service to add a new user. This service can be created inside of the Fizos.Customers.DataTable data table.   var customer = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape({ infoTableName : "InfoTable", dataShapeName : "Fizos.Customers.DataShape" }); var count = me.GetDataTableEntryCount(); var newEntry = {}; newEntry.ID = count + 1; newEntry.UUID = generateGUID(); newEntry.Type = Type; newEntry.Factors = "Fizos.CustomerTags:FirstTime"; newEntry.Name = Name; newEntry.Email = Email; newEntry.Address = Address; newEntry.Phone = Phone; customer.AddRow(); me.AddDataTableEntry({ sourceType: "Service", values: customer, source: "AddNewCustomer" }); We can adapt these for the customers that would rather not have accounts and be considered guests. Instead of the FirstTime data tag, you might want to add a Guest tag. For name, you could have it empty. The other fields, you’ll still want to likely have. This can give you insight into who these customers are that rather the guest checkout/ordering.     Step 4: Expanding Logistics Models   Let's do a quick review of what we have before we jump forward. In this Learning Path, we've setup scheduled factory inspections, machine automation, created customers, and setup order creation. What we're missing is the handling of deliveries.   In this learning path, we have talked about how to handle design aspects that could be held in a data table or have entities created to model each one. While there are many pros and cons to each method, we will do a mixture of both. Having the logistics data in data tables provide us with an easy form of querying data. Having entities match up with vehicles/transportation allows us to have greater tracking and live updates.   Let's create the vehicle/transportation data model, come up with logic on how to do deliveries from the factories we created earlier in this learning path, then setup a schedule or timer to kickstart the process.   Vehicles Data Model We already have our Data Table of vehicles. Let's create the templates and entities that will be a 1 to 1 between Thing and vehicle.   In the ThingWorx Composer, click the + New in the top left of the screen.   Select Thing Shape in the dropdown.   In the name field, enter Fizos.Vehicles.ThingShape and select a Project (ie, PTCDefaultProject). This Entity will have Services implemented by all types of vehicles. Save your changes and create three Thing Templates which implement this Thing Shape. See below for examples:   Fizos.Vans.ThingTemplate: These are smaller vehicles used to make short or last step deliveries.     Fizos.Trucks.ThingTemplate: These are trucks of different types making larger deliveries.     Fizos.Planes.ThingTemplate: These are planes used to deliver products to long distance locations.     Handling Shipping and Deliveries The cost of shipping and delivering goods is often the last thing people want to think about. Sometimes the cost of shipping goods is more expensive than the goods themselves. So how can we make this one of our strongest factors? By continuing trying to make our design simpler and less costly. We all know that it won't be an easy feat. The best way to do this is to have a system where we can have analytics and continuously improve on.   Let's start with the beginner steps of creating our straight-forward delivery service. Then, we will add Value Streams and tracking to see where we can make improvements. Finally, the solutions get better as we repeat these steps. No one solution is perfect, and no logic will be without holes or issues. Nevertheless, you continuously work on it, so that you can save cost and improve customer experience.   Open Fizos.Vehicles.ThingShape and go to the Properties tab. Create the following list of Properties. These properties are the generic concepts for a vehicle that can deliver a package. Name Base Type Aspects Details FuelCapacity Number 0 minimum, unit: liters logged, persistent AverageFuelConsumption Number 0 minimum, unit: liters logged, persistent MaxMass Number 0 minimum, unit: kilograms logged, persistent MaxVolume Number 0 minimum, unit: cubic meters logged, persistent CurrentLocation Location N/A logged, persistent CurrentOrders InfoTable(Fizos.Orders.DataShape) N/A logged, persistent Your properties should look like the following: Inside the Fizos.Vehicles.ThingShape entity, go to the Services tab. Create the following list of Services. These services are also generic in nature and are based on the concept of a vehicle going to a pick up location, goods being loaded onto the vehicle, the vehicle traveling to a destination, then delivering goods. Name Input Return Type Override Async Description PickUpGoods PickUpLocation: Location Nothing Yes Yes Go to a pickup location (factory or otherwise), and pick up goods. LoadGoods Orders: InfoTable (Fizos.Orders.DataShape) Nothing Yes Yes Perform the task of loading goods onto a vehicle (adding rows to the CurrentOrders property) Travel Destination: Location Nothing Yes Yes Travel for destination A to destination B DeliverGoods Orders: InfoTable (Fizos.Orders.DataShape) Nothing Yes Yes Perform the task of unloading goods at a current location As you can see, the goods are orders. In a real world environment, we would create a separate Data Shape and Data Table for packages to hold a number of orders. We are doing this without the packages Data Table for simplicity in this example. One reason why our products have mass and volume properties is to help with the idea of loading a vehicle and the type of boxes or packaging to use. This could be another way to cut cost. Your Services should look like the following: The same way we were able to create a system that was automated, we will create events that will notify subscribers of certain tasks being complete. This way, the next level of service can be performed instantly by our robot army. Inside the Fizos.Vehicles.ThingShape entity, go to the Events tab. Create the following list of events. These Events will connect to a task being completed. For example, when the vehicle has arrived to a location, an Event will be triggered and thus the next task can begin. For a bit more automation, add an Event you might think is needed, like when fuel is needed in the vehicle.   Name Data Shape Description DestinationReached AlertEvent This alert is fired when the vehicle has reached a location (whether for delivery or pickup). OrdersLoaded AlertEvent This alert is fired when all orders have been loaded onto a vehicle. DeliveryCompleted AlertEvent This alert is fired when the vehicle has completed a delivery. This delivery might have been the last order delivery and vehicle needs to head back for more orders to be picked up. Your Services should look like the following: Let's take a quick break to go over how this will work. A Service in the Fizos.Logistics Entity will search for all Things that implement the Fizos.Vehicles.ThingShape Entity. Each list of these Entities will have it's PickUpGoods Service called with the desired pickup location. When the destination is reached inside of the PickUpGoods Service, the DestinationReached Alert will be triggered. A Subscription waiting for this Event at the Thing level, will call the LoadGoods Service based on the condition of no orders being in the vehicle CurrentOrders Property. This LoadGoods Service will finish and trigger a OrdersLoaded Event. A subscription waiting for this Event at the Thing level, will call the Travel service. The Service will be called with the customer location as the destination OR the location of another site to perform other tasks. When the destination is reached inside of the Travel Service, the DestinationReached Alert will be triggered. A Subscription waiting for this Event at the Thing level, will call the DeliverGoods Service based on the condition of orders being left in the CurrentOrders Property. When the delivery is complete, the DeliveryCompleted Alert will be triggered. A Subscription waiting for this Event at the Thing level, will decide whether to go to a factory or pickup location to restart the process or wait for more instructions. You may have noticed a few things here. For starters, we are starting this from the Fizos.Logistics entity instead of a scheduler. For this process, you can start it with a scheduler, but being a 24 hour company, we don't have a schedule to start deliveries. That being said, the click of a button would do the job.   You can also see that we haven't given you the service code for some of these services. For some of these functions, they're almost duplicates of prior services. What will be more challenging and fun is the logic for which orders go to which delivery method. This is a mixture of vehicle properties, order properties, customer type, and customer location.     Step 5: Next Steps   Congratulations! You've successfully completed the Automated Distribution and Logistics guide.   In this guide, you learned how to: Create automated logistical processes Use services, alerts, and subscriptions to handle processes without human interaction Integrating complex logic with straight forward step by step systems   The next guide in the Complex and Automatic Food and Beverage Systems learning path is Securing Industry Data.    Learn More We recommend the following resources to continue your learning experience:   Capability            Guide Build ThingWorx Solutions in Food Industry Build Design Your Data 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 Help Center
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  Learn how to create systems to handle logistics and production distribution   GUIDE CONCEPT   This project will introduce complex aspects of the ThingWorx Composer and solution building.   Following the steps in this guide, you will develop your own IoT application or get a jump start in how to utilize ThingWorx for your needs.   We will teach you how to create a focused rules engine or service level logic to be used with the ThingWorx Platform.     YOU'LL LEARN HOW TO   Create automated logistical processes Use Services, Alerts, and Subscriptions to handle processes without human interaction Integrating complex logic with straight forward step by step systems   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete All parts of this guide is 60 minutes.      Step 1: Examples and Strategy   This guide builds on the knowledge gained in Factory Line Automation.   Download FoodIndustry.zip attached to this guide and extract/import the contents.   For the completed example, download the automated_food_industry.zip, also attached here. This download provides three Users and a Security Group. Please watch your count for Users or the import could fail.   In this tutorial we continue with our real-world scenario for the Fizos food company. We already have our factory data, and automated cooking processed for our sausage product lines. Now, let's see how we can use the data model we built before into making a smarter system of deliveries. We will take into consideration the locations of our factories, the vehicles we have available, our customers (stores and individuals), and see how much we can automate.   Setting Up Orders   One important part of being in business is having a product that people or companies want to buy. You'll need a way to track these sales and we're going to start with doing just that. Let's create our order shapes and tables.   In the ThingWorx Composer, click the + New in the top left of the screen.     Select Data Shape in the dropdown.     In the name field, enter Fizos.Orders.DataShape and select a Project (ie, PTCDefaultProject). All of our orders will be based off this Data Shape.     Click Save then Edit to store all changes now. Add the list of properties below: Name Base Type Aspects Description ID Integer 0 minimum, primary key, default 0 Row identifier CustomerId Integer N/A String used as unique identifer across multiple platforms Type String N/A Type of customer (individual or another company) Factors Tag Data Tag This will hold the different type of data points or tags that will help to analyze a customer's order Products Infotable Data Shape: Fizos.DataShapes.Products List of orders TotalPrice Number Minimum 0 Price of the order Status String N/A The current order status (ie, processed, shipped, completed, etc) Completed Boolean N/A Whether the order has been completed   The Properties for the Fizos.Orders.DataShape Data Shape are as follows:   6. In the ThingWorx Composer, click the + New in the top left of the screen.   7. Select Data Table in the dropdown and select Data Table in the prompt.   8. In the name field, enter Fizos.Orders.DataTable. Our differing types of customers will fall under this template. 9. For the Data Shape field, select Fizos.Orders.DataShape and select a Project (ie, PTCDefaultProject).     10. Click Save then Edit to store all changes now. 11. This Entity will be used to house our data and provide assistance with our analytics.   We now have our model of orders ready to be stored. Of course, our orders are simplified. We can add much more to get them rolling, but the most important aspect right now is our Factors field. While we know a ton of information about customers, we can also analyze what kind of products they're buying and their ordering habits.     Step 2: Expanding Customer Models    Let's start with our customers. We created the data shape for customers before when we decided to put them in data tables. This time, we'll add some customers, but also expand on our modeling of what a customer entails. In this step, as with all the steps in this learning path, you can go as granular as you like. If you'd like to make 100 data tags, then it helps with understanding your customer, but it might be too much based on your goals. Remember, more data means more processing of that data.   Creating Customer Data Tags   These data tags will help us decide priority, relationships, and much more.   In the ThingWorx Composer, click the + New in the top left of the screen.     Select Data Tag in the dropdown.     In the name field, enter Fizos.CustomerTags and select a Project (ie, PTCDefaultProject). Check the Dynamic checkbox. This allows for new vocabulary terms to be created during runtime.   Click on the Vocabulary Terms tab, and add the following terms and then add as many more as you see fit: Name  Store  Individual  Office  Company  FirstTime  Repeat  Partner  LongTerm  ShortTerm  Old  MiddleAged  Young  Loyal We just added a number of data tags based on customer type, age, times the customer has bought from us, etc. This will help us with characterizing and modeling our customers. We'll also cheat a bit and use these data tags to help with deliveries. If you're a partment brand, we might work faster to send goods. For example, the code below returns the list of orders that were made by partners. We can add this as a service to our Fizos.Logistics template.   var index; var customerId; var partnerObj = {}; var query = { "filters": { "type": "EQ", "fieldName": "Completed", "value": false } }; var orders = Things["Fizos.Orders.DataTable"].QueryDataTableEntries({ query: query }); var customers = Things["Fizos.Customers.DataTable"].GetDataTableEntries(); var result = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape({ infoTableName : "InfoTable", dataShapeName : "Fizos.Orders.DataShape" }); var partners = Resources["InfoTableFunctions"].TagFilter({ inclusive: true, fieldName: "Factors", t: customers, tags: "Fizos.CustomerTags:Partner" }); for(index = 0; index < partners.rows.length; index++) { customerId = partners.rows[index].ID; partnerObj[customerId] = true; } for(index = 0; index < orders.rows.length; index++) { customerId = orders.rows[index].CustomerId; if(partnerObj[customerId] === true) { var newEntry = new Object(); newEntry.ID = orders.rows[index].ID; newEntry.CustomerId = orders.rows[index].CustomerId; newEntry.Type = orders.rows[index].Type; newEntry.Products = orders.rows[index].Products; newEntry.Factors = orders.rows[index].Factors; newEntry.TotalPrice = orders.rows[index].TotalPrice; result.AddRow(newEntry); } } This code will retrieve all orders that are not in a completed state. It will then figure out which orders are for partners and return those orders. You can see in this simple example how Data Tags can be used.     Click here to view Part 2 of this guide.
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  Step 7: Import Extension   In the ThingWorx Composer. in the bottom left, click Import/Export, then select Import.   NOTE: The build produces a zip file in AuthenticatorExample->build->distributions folder. This zip file will be required for importing the extension.          2. For the Import Option field, select Extension.            3. Click Browse and choose the zip file in the distributions folder (located in the Eclipse Project's build directory).       4. Click Import to finalize the import.   Navigate to New Authenticator   Navigate to and select Security > Authenticators.            2. You will now see your CustomizedAuthenticator Authenticator as a option to view/edit.            3. Click Edit or View to see this new Authenticator. You wil notice the priority is 1.   Troubleshooting   If your import did not get through with the two green checks, you may want to modify your metadata.xml or java code to fix it depending on the error shown in the logs.     Issue Solution JAR Conflict arises between two similar jars JAR conflicts arise when a similar jar is already present in the Composer database. Try to remove the respective jar resources from the metadata.xml. Add these jars explicitly in twx-lib folder in the project folder inside the workspace directory. Now, build the project and import the extension in ThingWorx Composer once again. JAR is missing Add the respective jar resource in metadata.xml using the ThingWorx->New Jar Resource. Now, build the project and import the extension in ThingWorx Composer once again. Minimum Thingworx Version [ 7.2.1] requirements are not met because current version is: 7.1.3 The version of SDK you have used to build your extension is higher than the version of the ThingWorx Composer you are testing against. You can manually edit the configfiles->metadata.xml file to change the Minimum ThingWorx version to your ThingWorx Composer version.     Step 8: Integrating Custom Authentication   Integration can be handled in different methods based on your security needs and architecture. The Authenticator we created works with the demo site we provided in the download.   Create ThingWorx User   Let's create the User that we will be processing login attempts.   In the ThingWorx Composer, click the + at the top left of the screen.   Select User in the dropdown.   Set the Project (ie, PTCDefaultProject) and enter the Name for this new user as TwxUser. Enter the Password for the new user to something easy to remember (ie, 2020Password2021).   Click Save.   Create Logger   Create a helpful logger for authentication attempts.   Create a Thing named AuthenticationStamper to go with the LoginHelper variable in the earlier Java source code. Ensure this new Thing inherits from the RemoteThing Match the properties for the AuthenticationStamper with the two properties in the below image.   Save the changes.   Create Login Helper   Create a stream for authentication attempts for helpful tracking. Create a ValueStream named AuthenticationStream and Save the Entity. In the Properties and Alerts section of the ValueStream, click Manage Bindings.   Setup the binding to match the below configurations. Bind the properties to the AuthenticationStamper Thing.   Save the changes made to the ValueStream.   Demo Page Login   With a browser, open the TestSite.html web page found in the Demo directory of the download. You should see a login page similar to that as the below image.   Enter the name of the user we created, TwxUser. Enter the password for the user. We suggested setting this to Password2019. Click Submit. You will now be authenticated and logged in.     Step 9: Next Steps   Congratulations! You've successfully completed the Create An Authentication Extension tutorial, and learned how to:   Install the Eclipse Plugin and Extension SDK Create and configure an Extension project Create Authentication Application Build and import an Extension   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Application Development Tips & Tricks   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Extension Development Guide    
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  Guidelines for selecting the optimal method for connecting to ThingWorx   GUIDE CONCEPT   In the world of IoT application development, connectivity refers to the infrastructure and protocols which connect devices to the cloud or network. Edge devices handle the interface between the physical world and the cloud.   ThingWorx provides you with several different tools for connecting to the Thingworx platform.   This guide is designed as an introduction to these tools, and will help you determine which to choose based on your specific requirements.   YOU'LL LEARN HOW TO   Pros and cons of different connection methods The connection method best suited for some typical applications Where to find detailed information about any connection method   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 30 minutes   Step 1: Connectivity Method Options   There are many factors that will influence your decision about the ideal mechanism to connect to ThingWorx. On this page we compare and contrast different methods and give examples for where each one is a natural fit.   Connectivity Method Developer Benefit REST API Integrate seamlessly using dynamically-generated API calls Azure IoT Hub Connector Connect devices that use Azure IoT Hub Edge SDKs Build full-featured integrations for any platform ThingWorx Kepware Server Connect out-of-the-box with over 150 protocol drivers for industrial equipment Edge MicroServer Establish bi-directional connectivity with this complete, ready-to-run agent   REST API   Pros Cons Typical use case Skills Required Connection Type  Web developer can easily create integration ThingWorx cannot trigger action on the edge Push data from small devices to ThingWorx REST API development Request/Response   Using the ThingWorx REST API is an easy way for low-capability devices to connect with a ThingWorx platform. Any edge device that can make an HTTP POST can read and update properties or execute services on a ThingWorx platform. The disadvantage of this method is that it is one way from edge to platform. There is no way for the platform to initiate a service on the remote device and properties are only updated when the edge device initiates a connection with ThingWorx.   Learn more about the ThingWorx REST API:   Use REST API to Access ThingWorx Using the Connect an Arduino Developer Board tutorial REST API Documentation   Azure IoT Hub Connector   Pros Cons Typical use Case Skills Required Connection Type  Easily connect devices that use Azure IoT Hub Adds dependency and cost to application Add ThingWorx for devices connected with the Azure cloud Azure edge development AlwaysOn™   The diagram illustrates device-to-cloud integration with the Azure IoT Hub.   The ThingWorx Azure IoT Hub Connector establishes network connections to both ThingWorx Foundation and the Azure IoT Hub. Data flows in from devices, through the Azure IoT Hub hosted in the cloud, to the ThingWorx Azure IoT Hub Connector configured for a specific ThingWorx instance. The ThingWorx Azure IoT Hub Connector translates messages from the Azure IoT Hub format, to the native ThingWorx format and uses an established AlwaysOn connection to forward the information to ThingWorx Foundation.   Azure IoT Hub   Connect Azure IoT Devices   Edge SDKs   Pros Cons Typical Use case Skill Required Connection Type  Fully integrate device or remote system with ThingWorx platform Most developer flexibility All functionality must be developed by programmer Full customization or tight integration required Application development in Java, C, or .Net AlwaysOn™   These SDKs are developer tools that wrap the protocol used to connect to the ThingWorx Platform. There are SDK's available for Java, C, and .Net languages. The Edge MicroServer uses the C SDK internally. All SDKs use the ThingWorx AlwaysOn binary protocol together with the HTTP WebSocket protocol for transport. WebSocket connections can operate through a firewall allowing two-way, low latency communication between the device and server. The SDKs support the following key concepts that allow a Thing developed with an SDK to be a full-fledged entity in the ThingWorx environment:   Remote properties — Entities that define the types, identities, and values from a device or remote system Services — Actions that can be performed on demand by a device or remote system Events — Data that is sent to a subscribed device or remote system whenever the Event is triggered   You can choose from any of the SDK's to create a custom application that meets their exact requirements.   C SDK   The C SDK is the most lightweight of all the SDKs and will result in an application that uses the least amount of RAM, frequently requiring less than 200kB. It is the only SDK that is distributed as source code, allowing compilation of C SDK applications on any platform even those without an operating system.   Learn more about the C SDK:   C SDK Tutorial C SDK Documentation   Java SDK   The Java SDK is designed for portability and simplicity to ease connecting any Java-enabled device or system to ThingWorx. The Java SDK is provided as .jar files and sample Java source code. Any system that can run Java 1.7.51 or later should be able to build and run the example applications.   Learn more about the Java SDK:   Java SDK Tutorial Java SDK Documentation   .Net SDK   The .Net SDK is provided as .dll files with sample Visual C# project files and source code. Any system that can run Microsoft NET 3.5 SP1 Framework development environment should be able to build and run the example applications.   Learn more about the .Net SDK:   .Net SDK Documentation   ThingWorx Kepware Server   Pros Cons Typical Use case Skill Required Connection Type  Easily connect to hundreds of different types of industrial equipment Requires computer running Windows physically connected to device Adding ThingWorx to an industrial setting Configure settings AlwaysOn™   The ThingWorx Kepware Server Windows client lets users quickly and easily connect real-time, bi-directional industrial controls data to the ThingWorx IoT Platform via the ThingWorx AlwaysOn protocol. ThingWorx services enable users to browse, read, write, and interact with ThingWorx Kepware Server, and includes intuitive tools that simplify the modeling of industrial things.   Learn more about the ThingWorx Kepware Server:   Connect Industrial Devices and Systems ThingWorx Kepware Server Documentation ThingWorx Kepware Server Manual   Edge MicroServer   Pros Cons Typical Use case Skill Required Connection Type  Easily connect with simple scripting Requires a device running Windows or Linux Customization with Lua scripting Connecting gateway router to ThingWorx Configure settings AlwaysOn™   The ThingWorx Edge MicroServer is a binary executable available for Windows and Linux running on either ARM or x86 processors. The EMS establishes an AlwaysOn, bi-directional connection to a destination ThingWorx platform when it is started. The EMS is configured by editing a json text file to specify the target platform and credentials. The EMS uses the always on connection to provide a local HTTP server that is a reflection of the platform REST API. This local copy of the platform API allows devices that are not capable of making encrypted connections across the open internet to securely interact with the platform. The EMS package also includes the Lua Script Resource application. This application extends the ThingWorx Foundation server by connecting through the EMS HTTP server and provides a Lua interpreter that can be used to connect local resources to the ThingWorx server.   Learn more about the ThingWorx Edge MicroServer:   Connect a Raspberry Pi to ThingWorx using the Edge MicroServer Edge MicroServer Documentation   Step 2: Next Steps   Congratulations! You've successfully completed the Choose a Connectivity Method guide.   At this point, you can make an educated decision regarding which connection methods are best suited for your application and infrastructure.   The next guide in the Connect and Configure Industrial Devices and Systems learning path is Use REST API to Access ThingWorx   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Connect ThingWorx Application Development Reference Build Get Started with ThingWorx for IoT Experience Create Your Application UI   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support ThingWorx Connectors Help Center
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  Connect IoT data from devices to Widgets that display in your application UI.   GUIDE CONCEPT   This project will demonstrate how to bind a data source to a Widget.   Following the steps in this guide, you will be able to show state-based changes resulting from data updates.   We will teach you how to essentially connect your backend data to the Widgets in your Mashup. ThingWorx facilitates this process with built-in functionality.     YOU'LL LEARN HOW TO   Bind data to Widgets in ThingWorx Mashup   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 30 minutes.      Step 1: Completed Examples    Download the Complete Data Binding Example using file BindDataEntities.zip attached to this guide. Within this file you, you will find Entities referenced in this lesson, including a finished application. Import and utilize this file to see a finished example and return to it as a reference if you become stuck during this guide and need some extra help or clarification.   Keep in mind, this download uses the exact names for Entities used in this tutorial. If you would like to import this example and also create Entities on your own, change the names of the Entities you create.     Step 2: Bind Data to Widget   In order to display data from connected devices, each Widget must be connected to a data source. You should already be familiar with how to find the Widgets in the top left panel of a Mashup screen.   Mashup Areas   In the top right of the screen in a Mashup Entity is the Data Panel. This is where Entities and Services are used to bring in data and added functionality. This area also includes the Session Tab, which includes data that is being stored in the session. You can learn more about that in the Create Session Parameters guide. You can also filter for specific Properties.       In the lower right of the screen in a Mashup Entity is the Data Properties Panel. This is where you can configure how your Service calls will react to different Events. For example, you might want to perform a call to a Service as soon as another Service call is complete. You'll do that in this section. You will also notice the Functions Tab. This table enables you to create custom functionality for you Mashup and Widgets, such as navigating to a different Mashup on the click of a button.       In the lower left of the screen in a Mashup Entity is the Widget Properties Panel. This is where you'll be able to not only customize your Widget, but connect data directly from Services and other Widgets. You will also notice the Style Properties Tab. This will provide access to change the styling and themes used for a Widget. You can also filter for specific Properties.       In the bottom middle of your screen, you'll notice the Bindings Panel. This panel shows you where your connections are in reference to Widgets, Services, and any Events that are being used to connect them. Whenever you have a problem with thinking about the flow of your Mashup, look down to this panel to get a quick idea. You'll also notice the Reminder Tab in this area. This tab just helps with things you might have forgotten to do when setting up your data binding, such as setting the display field for a Widget.       Let's now move forward with setting up our data binding.    Add Service   Open the HelloWorldPlayground Mashup. Drag and drop the Grid Advanced Widget to the left-hand column of your Canvas.      NOTE: If a pop-up appears about adding a Panel, choose Yes. 2. Click + in the Data panel.       3. Search and select the HelloWorldData Data Table from the search bar in the top left of the home screen. 4. Search for the GetFirstEntry Service and click the blue arrow. The GetFirstEntry service is part of the DataMagicians.XML file you imported.   5. Check the Execute on Load checkbox. This makes the page automatically load with content for a Widget. 6. Click Done.     Bind Data to Widget   We will bind data to a Widget, but also make the Widget editable. When it comes to making the field Editable, keep in mind any connections or Entities involved. In this case, the World field is attached to an Entity.   In the Data panel, expand the Returned Data section. Drag and drop the All Data field of the GetFirstEntry Service to the Grid Advanced Widget. A pop-up will appear to select the binding target, select Data.   In the Widget Properties panel, check the checkbox for IsEditable. This will allow users to edit the data in the Property fields. Select the Configure (Gear) button. The Configure Grid Columns window will open.   Update any fields you would like hidden (for instance, uncheck the box next to timestamp and key).     Click on source field. Check the Editable checkbox at the bottom.   Click Done to close the pop-up.   Data Panel Buttons   There is an assortment of helpful buttons in the Data panel to make our lives easier.     Top Row of Buttons   The + button that you used before adds more Entities as a resource to make Service calls. The circlular button next to it provides a reload functionality. This is useful when you've made a change to a Service and would like for it to appear here. For example, adding a new parameter to a Service call.   Buttons by Entity   The i button provides information and access to the Entity in question. The Add Service button adds a Service that belongs to the same Entity. This will definitely help with saving time. The last button is the Delete button. This will delete the Entity from the list of resources that can make Service calls.   Buttons By Service   The only button is the Delete button. This will delete the Service from the list of Service calls an Entity has available.   Adding More Functionality   Click the Add Service button on the HelloWorldData Entity in the Data pane.     Search for the UpdateDataTableEntry Service and click the arrow to select it. Leave the Execute on Load checkbox unchecked and click Done. Deselect the Execute on Load option if you would like the page to load with content in response to user input, rather than automatically at startup. Click on the UpdateDataTableEntry Service, then click on the Data Binding button in the bottom right. Click the arrow next to the Values Property under Parameters. Click Add Source. When the list of Widgets appear, click on the Editedtable of the Grid Advanced Widget that we added. Click Next and Done. Select the Grid Advanced Widget on your canvas. Drag and drop the EditCellCompelted property onto the UpdateDataTableEntry service.     NOTE: The columns returned by a data service are shown under the All Data section of the data service. They are outbound bindable, indicated by the outward facing arrows. When a data service All Data or individual column is bound, the arrows become filled in. You can also bind the data from one Widget to another.         8. Click Save and View Mashup.   You have just bound data from a service to a Widget. There are many different Widgets, and the process for binding data to a Widget is often similar. Within Composer, you can simply drag and drop to bind configurations for Events and Properties.     TIP: As, an extension to this lesson, edit the Property Display Widget to update the entry in the HelloWorldData DataTable. Editing the name of the World will result in no entries being updated. To add or update an entry when you update the Name property, used the AddOrUpdateDataTableEntry service instead of UpdateDataTableEntry.     Step 3: Next Steps    If you have questions, issues, or need additional information, refer to:   Resource Link     Community Developer Community Forum Support 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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  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 - Video Guide   This project will introduce ThingWorx Analytics Builder via a convenient video-guide. Following the steps in this video, you will create an analytical model, and then refine it based on further information from the Analytics platform. We will teach you how to determine whether or not a model is accurate and how you can optimize both your data inputs and the model itself.     ThingWorx Analytics Server Installation with SSL   This video demonstrates the installation of Analytics Server with SSL protections enabled. It includes information about generating the necessary certificates and truststores to enable SSL for each component that connects with the server.     ThingWorx Platform Analytics Installation with SSL   This video demonstrates the Platform Analytics installation with SSL protections enabled. It provides information about generating the necessary certificates and truststores to enable SSL for all connected components, including RabbitMQ and Flink.     Toolbar Widget | ThingWorx 9   Watch this video to learn how to add a Toolbar widget to a mashup in ThingWorx. You'll learn how to define actions using a data service and create bindings to control and configure a Grid widget.     ThingWorx SSO: Login demonstration from Azure AD to ThingWorx   This video demonstrates the ThingWorx SSO login procedure using Azure AD. The login procedure is shown from a user point of view. Then a behind the scenes view looks at the design in ThingWorx Composer.     Menu Bar Widget ThingWorx 9   Watch this video to learn how to create a mashup layout that uses a Menu Bar widget for navigation. You'll learn how to create a layout, define menu items, and configure the widget.     ThingWorx AD FS SSO Setup   This video provides a walk-through of the steps required to set up SSO for ThingWorx in an environment where AD FS is both the CAS and the IdP. The focus is on the AD FS setup steps.     Create Your Application UI   Following the steps in this video-guide, you will learn how to use the ThingWorx Mashup Builder tool to create a Graphical User Interface (GUI) for your IoT Application. We will teach you how to rapidly create and update a Mashup, which is a custom visualization built to display data from devices according to your application's business and technical requirement     Get Started with ThingWorx for IoT   Explore the ThingWorx Foundation Internet-of-Things application building platform in a convenient, instructional video guide format.     Thingworx Mashup 101 - Do's and Don'ts   This session covers the most common and useful tips about how to correctly use Mashup builder, Widgets and Layouts – and what to avoid - to create applications with good principles of UI/UX and easier to maintain.     What's New in ThingWorx 9.1   The industry’s most complete IIoT platform just got better. Loaded with a full range of new and updated features and functionality, you can expect powerful platform capability enhancements across the board.       Standardize Connectivity to Devices, Applications, & Systems for Centralized IIot Data   You can’t implement IIoT solutions if you can’t connect to your assets, but in complex environments connectivity can seem like an insurmountable challenge. ThingWorx makes it easy to establish standardized connectivity, so you can create a secure, single-source for accessing industrial data across your IT and OT systems     Bar Chart Widget | ThingWorx 9   Watch the following video on how to add the Bar Chart widget to a mashup and configure basic widget properties.     Button Widget | ThingWorx 9   Watch the this video to learn how to add the Button widget to a mashup and use its Clicked event to trigger data services.     Line Chart Widget | ThingWorx 9   Watch the this video to learn how to add the Line Chart widget to a mashup and bind a data source.     ReImagine Your Application UI With Collection and Custom CSS   Create compelling, modern application user interfaces in ThingWorx with the latest enhancements to our Mashup visualization platform - Collection and Custom CSS. In this webinar with IoT application designer Gabriel Bucur, we'll show how the new Collection widget makes it easy to replicate visual content in your UI for menu systems, dashboards, tables, and more.     Testing Your Edge Application   Native testing is an often-overlooked aspect of edge application development. This session shares how to perform thorough testing before you push your application into live production.     Predictive Maintenance with Thingworx 101   Adopting a predictive maintenance strategy for the first time can often feel daunting, but it doesn't have to. Learn how ThingWorx can be used to leverage edge devices and turn monitoring activity into action.     ThingWorx Mashup Pitfalls: What to Avoid and What Not to Do   Designing Mashups takes time. Moreover, expanding upon a Mashup that was built for a small-scale application can get cumbersome.     IoT Security: Keeping Devices Safe in a High Risk World   Understanding access controls can be difficult. With the built-in security features and concepts in ThingWorx, you can easily give your devices access on-demand.     Edge Connectivity in Unreliable Networks   The Store and Forward feature within ThingWorx Industrial Connectivity assures users that critical data collected at the edge won't get lost during an outage.  
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    Step 3: Test Services and Save Test Cases   In the previous step, you created an Entity with a customized Service. You can easily test and update the Service within the same editing window. This allows you to quickly modify a Service and confirm that its new behavior is functioning as expected. Test Execution   To execute a Service, you can do it while still developing and after you have created it.   During Development   Open the LineCheckSystem Thing. Select the Services tab. Click the ListHotLineParts link.   If you scroll to the bottom of the page, you will see the Execute window. Enter a value for the TemperatureThreshold and click Execute.     After Development   Open the LineCheckSystem Thing. Select the Services tab. Click the ListHotLineParts Execute play button.   Enter a value for the TemperatureThreshold and click Execute.       Saving Test Cases   Store input parameters makes testing during development and after development much faster. Whether you are testing base cases, specific scenarios, or throwing everything you possibly can at your Service, this tab will allow you to store and name test cases for later usage. During Development   Open the LineCheckSystem Thing. Select the Services tab. Click the ListHotLineParts link   If you scroll to the bottom of the page, you will see the Execute window. Enter a value for the TemperatureThreshold and click Save Input Set. When prompted, enter a name for this test case and click Save Input Set.   After Development   Open the LineCheckSystem Thing. Select the Services tab. Click the ListHotLineParts Execute play button.   Enter a value for the TemperatureThreshold and click Save Input Set.   When prompted, enter a name for this test case and click Save Input Set.   While this example is pretty simple and straightforward, it is also possible to store other values and other data types! You will be provided with a dropdown of your stored test cases. Just select a case and click Execute.     Step 4: Utilize Code Auto-Complete Feature   If it is not open already, open the ListHotLineParts Service or create a new one. On a new line, enter ThingTemplates["LinePartTemplate"]. Add a period . after the closing bracket to gain access to the Services and Properties of the Thing Template we created earlier.     You will now see the Services and Properties that were created inside of the LinePartTemplate or passed down in its object-oriented model.     Step 5: Test Code at Design Time with Linting   Linting is a process by which your development environment warns you of possible issues even before any attempt to run or test the code. Instead, an in-editor warning pops up that alerts you to the issue as you’re writing your code. Linting is yet another feature which many IDEs provide outside of the IoT realm.   Select the Lint checkbox at the top of the Service-editing section.   Type any statement that would cause a Lint warning or error to pop up. In this case, it is best programming practice to use a semi-colons at the end of JavaScript code.   Debugging   Services within the composer are based on the Rhino JavaScript engine, which is built using Java.   It is currently unavailable to see the JavaScript code running within the browser, but you are able to still log what is occurring at each step of development and runtime. ThingWorx provides logging that will go to the ScriptLog under the Monitor section on the left. The logger uses different log levels that will appear differently in the ScriptLog screen.   Level Syntax Example info logger.info logger.info("X + 1 = " + 5); trace logger.trace logger.trace("Print this InfoTable - " + table.toJSON()); warning logger.warn logger.warn("Print random JSON data - " + JSON.stringify(data)); debug logger.debug logger.debug("Adding debug information here."); error logger.error logger.error("What kind of error took place? " + err.message); Testing Tips   From the Lint website, here are some common mistakes that JavaScript Lint looks for: Missing semicolons at the end of a line. Curly braces without an if, for, while, etc. Code that is never run because of a return, throw, continue, or break. Case statements in a switch that do not have a break statement. Leading and trailing decimal points on a number. A leading zero that turns a number into octal (base 8). Comments within comments. Ambiguity whether two adjacent lines are part of the same statement. Statements that don't do anything. JavaScript Lint also looks for the following less common mistakes: Regular expressions that are not preceded by a left parenthesis, assignment, colon, or comma. Statements that are separated by commas instead of semicolons. Use of increment (++) and decrement (--) except for simple statements such as "i++;" or "--i;". Use of the void type. Successive plus (e.g. x+++y) or minus (e.g. x---y) signs. Use of labeled for and while loops. if, for, while, etc. without curly braces. (This check is disabled by default.)     Step 6: Next Steps    Congratulations! You've successfully completed the Application Development Tools, Tips & Tricks, and learned how to:   Use Snippets to generate code Execute and test Services Save Service test cases to facilitate QA process Utilize the code auto-completion feature Test code at design time with Lint warnings and errors   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Build Create Custom Business Logic Guide Experience Create Your Application UI     Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Help Center
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  Step 5: Widget Properties   You can configure Properties to change the style of the map as well as specify a custom image to use for map markers. Other Properties allow two linear paths and polynomial regions to also be displayed on the map Widget with optional tooltip data.   Bindable   Name Type  Default Direction  Description Data Infotable None Input Source for the data that is displayed as discrete markers LocationField MenuName None Input Field that contains Location type data to plot markers MarkerField MenuName None Input Field that contains data of type Image used to plot markers ShowMarkerTooltip Boolean True Input Hovering over map markers will display a tooltip when set to true ToolTipField1 thru 4 MenuName None Input Optional field displayed in tooltip when the user hovers over a map marker ToolTipLabel1 thru 4 String None Input Optional label for tooltip data RouteData Infotable None Input Source for the data that is displayed as a connected route RouteLocationField MenuName None Input Field that contains Location type data to plot connected route line ShowRoute Boolean False Input Show route data on map when set to true PlannedRouteData Infotable None Input Source for the data that is displayed as a second connected route PlannedRouteLocationField MenuName None Input Field that contains Location type data to plot a second connected route line ShowPlannedRoute Boolean False Input Show planned route data on map when set to true RegionData Infotable None Input Source for the data that is displayed as a region RegionLocationData Infotable None Input Region location data source RegionLocationsField MenuName None Input Field which will provide location table information for region RegionLayerField MenuName None Input Field that contains Numeric data used for region layer number ShowRegions Boolean False Input Show region data on map when set to true ShowRegionTooltips Boolean True Input Hovering over region will display a tooltip when set to true RegionToolTipField1 thru 4 MenuName None Input Optional field displayed in tooltip when the user hovers over a region RegionToolTipLael1 thru 4 String None Input Optional label for region tooltip data RegionFillOpacity Number 1 Input Opacity of region fill color from 0 being transparent to 1 being opaque SelectedLocation Location None Input/Output The currently selected location CurrentZoom Number 8 Output The currently displayed zoom level ( 1 - 15 ) Zoom Number 8 Input Number used to set the zoom level ( 1 - 15 ) ShowMarkers Boolean True Input Shows map markers if set to true ShowPathMarkers Boolean True Input Shows map markers if set to true ShowTraffic Boolean False Input Shows traffic data color overlay on map if set to true NEBoundary Location None Output The northeast boundary location NWBoundary Location None Output The northwest boundary location SEBoundary Location None Output The southeast boundary location NWBoundary Location None Output The southwest boundary location Visible Boolean True Input Widget is visible if set to true     Static   Name Type  Default Description DisplayName String None Name used for widget in user facing interactions Description String None Description used for widget in user facing interactions MapType Roads/Satellite/Hybrid/Terrain Roads The type of map content displayed MapSkin Normal/Blue/Grey Normal Options for styling maps monochromatically AutoZoomBehavior Data change/Initial Data only Data Change Controls when map will automatically zoom to show all markers ClusterLocations Boolean False Combines multiple location markers that are near one another into a single marker if true MultiSelect Boolean False Enable multiple marker selection   Widget Events   DoubleClicked- Triggered when user double clicks on the Google Map. Changed- Triggered when the data for this widget is modified. BoundsChanged- Triggered when the bounding box of the displayed map changes.     Step 6: Next Steps   If you have questions, issues, or need additional information, refer to:   Resource     Link Community Developer Community Forum Support Google Map Widget Help Center Free Google Maps Widget Extension IQNOX  
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  Add a Google Map to your UI that visually presents geographical data.   GUIDE CONCEPT This project will introduce how to visually present geographical data in your application. Showing data on a map is a valuable feature for IoT application.   Following the steps in this guide, you will utilize the Google Maps Widget and explore it’s ability to show multiple Things.   We will teach you how to use Geological data to convey pertinent information in your UI.   YOU'LL LEARN HOW TO   Download and import the Google Maps Widget extension Create a Mashup and add a Google Maps Widget Configure the Google Maps Widget to display the locations of multiple Things   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete ALL 2 parts of this guide is 30 minutes.      Step 1: Configure Google Maps Widget   When you download the Google Maps Widget, it does not include an API key. Google allows some limited use of their map API without a key, however it is recommended that you obtain and add your own Google Maps API key or Client ID (for Google Maps API for Work licenses) to the Google Maps Widget.   The ThingWorx hosted server has already been configured with the Google Maps Widget, including an API key for evaluation use.   Refer to the Google Maps API documentation to obtain your own API key, then follow the steps below.   Download the Google Maps widget from PTC Partner, IQNOX NOTE:  It is necessary to create an account on IQNOX, but the download is free       2. In the lower-left side of Composer, click Import/Export, then Import.           3. In the Import From File pop-up, under Import Option select Extension from the drop-down, then click Browse        4. Navigate to the .zip file you downloaded.          5. Click Import in the Import From File pop-up, then click Close after file is successfully imported.        6. In the ThingWorx Browse tab, in the System section, click on Subsystems, then PlatformSubsystem.            7. Click on the Configuration tab, then click the Edit button if you are not already in edit mode.        8. Scroll down to the Required string to connect with Google for Google widgets field where you enter the Google Maps JavaScript URL with your API key: https://maps.googleapis.com/maps/api/js?key=<Your API key>            9. Click Save     Step 2: Add Google Maps Widget to Mashup   Click the Browse folder icon on the top left of ThingWorx Composer.       2. Select Mashups in the left-hand navigation, then click + New to create a new Mashup.                          3. For Mashup Type select Responsive.               NOTE: A Responsive Mashup scales with a browser's screen size. In the steps below we will create 5 containers, one for each widget, to organize how the widgets are presented.        4. Click OK.        5. Enter a name for your Mashup.        6. If Project is not already set, click the + in the Project text box and select the PTCDefaultProject.        7. Click Save        8. Select the Design tab to display Mashup Builder.        9. Click the Widget tab on the top-left, then enter map inside the Filter Widgets field.        10. Drag-and-drop the Google Map widget onto the Mashup.         Step 3: Download Sample Entities   We have created a file with sample entities for this exercise.   Download demoTractors.xml to your computer.       2. Click Import/Export in the lower left of Composer, then select Import          3. Click From File in the drop down, then Browse.        4. Browse to the demoTractors.xml file and click Open.        5. Click Import, then Close after entities are successfully imported.     Step 4: Add Markers to Google Maps Widgets   To display markers on the map you must bind an Info Table to the Widget's Data Property and specify which column has location information. In this example we will use the QueryImplementingThingsWithData Service to bind a group of Things created with the same Template.   Click the + button in the Data tab on the right side of the Mashup Builder window             2. In the Add Data pop-up, click Thing Templates from the Entity Type drop down.                3. Select a Template from the list that has a Location property and was used to create Things. For this exercise, use the ConnectedTractor Template that was imported from the sample file downloaded in the prior step.            4. Enter query into the filter text box then click the arrow to the right of QueryImplementingThingsWithData.                                              5. Click the Execute on Load check box, then click Done. This will cause the QueryImplementingThingsWithData Service to execute as soon as Mashup is loaded.                                          6. Expand Returned Data if you do not see All Data then click and drag All Data onto the Google Maps Widget.        7. Click Data in the Select Binding Target pop-up.          8. In the Properties panel in the lower left, scroll to see the LocationField property and select Location, the name of the Property with location information.       Test Map Operation   Click Save to save your Mashup.       2. Click View Mashup to see the Google Maps Widget displaying the locations of each Thing.     NOTE: The Google Maps Widget has built-in functionality that allows users to pan and resize the map as well as switch to satellite photo maps.     Click here to view Part 2 of this Guide.
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    Step 12: Connect to Temperature Sensor   This step is optional. Additional instructions are provided for developers who are interested in interfacing with sensors.   The DHT11 and DHT22 digital temperature and humidity sensors are inexpensive and available from several sources: Adafruit Sparkfun SeeedStudio The Raspberry Pi does not come with any built-in analog to digital conversion capability and because these sensors are digital they can be interfaced easily with a Raspberry Pi. We will be using a Python library developed by Adafruit that simplifies interfacing with these sensors.   Install Adafruit Python Library for Sensors   We will use Git to download the Adafruit DHT11 Python from GitHub. Check if Git is already installed by opening a command window and typing the command: git If you see a "command not found" error message use this command to install Git: sudo apt-get install git-core If you get an error installing Git, run the command: sudo apt-get update then try to install Git again. Change into the EMS directory: cd microserver Download the Adafruit library with this command: git clone https://github.com/adafruit/Adafruit_Python_DHT.git Change into the directory that was just downloaded: cd Adafruit_Python_DHT Install Python build libraries: sudo apt-get install build-essential python-dev Build and install the library with this command: sudo python setup.py install   Connect Sensor to Raspberry Pi Power down the Raspberry Pi before making any wire connections. To prevent any flash memory corruption, enter the command shutdown -h now then wait a few seconds for it to complete before disconnecting the power supply. Use female-to-female jumper wires to connect the sensor as shown below. The black wire is connected to ground, the red wire is 5v or VCC, and the yellow wire carries is the digital signal. WARNING: Double check your connections before applying power. Mistakes can destroy the sensor and the Raspberry Pi!   3. Apply power and boot the Raspberry Pi. 4. Change into the EMS directory:   cd microserver 5. Test the sensor with this commmand:   ./Adafruit_Python_DHT/examples/AdafruitDHT.py 11 4   In a few a seconds the current temperature and humidity will be displayed. Change the 11 parameter to 22 if you are using the DHT22 sensor. The 4 parameter is the GPIO pin number of the Raspberry Pi that is conneCted to the sensor's signal pin. This command is the same command the luaScriptResource will use to get temperature and humidity readings.   Modify Lua template file A dozen lines need to be added to the file PiTemplate.lua file in the /microserver/etc/custom/templates directory.   After the line: properties.cpu_volt = { baseType="NUMBER", pushType="ALWAYS", value=0 } Add the two lines: properties.temp = { baseType="NUMBER", pushType="ALWAYS", value=0 } properties.humidity = { baseType="NUMBER", pushType="ALWAYS", value=0 } After the line: local voltCmd = io.popen("vcgencmd measure_volts core") Add the line: local sensorCmd = io.popen("./Adafruit_Python_DHT/examples/AdafruitDHT.py 11 4") After the line: properties.cpu_volt.value = s Add these 9 lines: -- set property temp and humidity local sensor = sensorCmd:read("*a") log.debug("[PiTemplate]",string.format("raw sensor %s", sensor)) s = string.match(sensor,"Temp=(%d+\.%d+)"); log.debug("[PiTemplate]",string.format("scaled temp %.1f", s)) properties.temp.value = s s = string.match(sensor,"Humidity=(%d+\.%d+)"); log.debug("[PiTemplate]",string.format("scaled humidity %.1f", s)) properties.humidity.value = s Stop and then restart luaScriptResource by using the following commands. ps -efl Will list all running processes, note the number in the PID column for ./lusScriptResource kill -9 PID# Replace PID# with number noted above and the process will be ended. Run LuaScriptResource by executing the following command: sudo ./luaScriptResource   Update Properties of PiThing   Log onto ThingWorx Foundation server. Click on the Home icon in Composer then broswse to Things > PiThing > Properties and click Manage Bindings button.   Click on the Remote tab, then drag and drop the temp and humidity Properties one at a time to the green plus sign next to Create new Properties. Click Done to close the binding window, then click Save. NOTE: The temp and humidity Properties will be updated every 30 seconds.     Step 13: Next Steps   Congratulations! You've successfully completed the Connect Raspberry Pi to ThingWorx guide, and learned how to:   Set up Raspberry Pi Install, configure and launch the EMS Connect a remote device to ThingWorx   Learn More   We recommend the following resources to continue your learning experience:   Capability Guide Manage Data Model Introduction Build Get Started with ThingWorx for IoT   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource Link Community Developer Community Forum Support Edge SDKs and WebSocket-based Edge MicroServer (WS EMS) Help Center External Raspberry Pi Documentation  
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  Step 8: Configure Template File (Service)   Services are implemented as Lua functions. In our Lua script, Services are divided into two pieces. The first is the Service definition which consists of a Service name, inputs and output. The second part of defining a Service is the service code. The Service code is run when you execute the service.   Create Service Definition Open the PiTemplate.lua file. Append the service definition to the file. Create a service named GetSystemProperties that gets the system properties (cpu temperature, clock frequencies, voltages) from your Raspberry Pi and updates the respective properties on the Thingworx platform. Specify your output type but not the name because the name of every output from a ThingWorx service is always result. serviceDefinitions.GetSystemProperties( output { baseType="BOOLEAN", description="" }, description { "updates properties" } ) NOTE: This service has no input parameters and an output that results in True if the properties were successfully updated on Thingworx.   Create Service code The Service code is run when you execute the Service. Functions in Lua are variables therefore to define the Service code, you will create a variable. The name of the Service has to match the name you specified in the Service definition.   Copy the service code below with comments explaining the logic and add append it to your template file, or download and unzip the full PiTemplate.zip attached here. services.GetSystemProperties = function(me, headers, query, data) log.trace("[PiTemplate]","########### in GetSystemProperties#############") queryHardware() -- if properties are successfully updated, return HTTP 200 code with a true service return value return 200, true end function queryHardware() -- use the vcgencmd shell command to get raspberry pi system values and assign to variables -- measure_temp returns value in Celsius -- measure_clock arm returns value in Hertz -- measure_volts returns balue in Volts local tempCmd = io.popen("vcgencmd measure_temp") local freqCmd = io.popen("vcgencmd measure_clock arm") local voltCmd = io.popen("vcgencmd measure_volts core") -- set property temperature local s = tempCmd:read("*a") s = string.match(s,"temp=(%d+\.%d+)"); log.debug("[PiTemplate]",string.format("temp %.1f",s)) properties.cpu_temperature.value = s -- set property frequency s = freqCmd:read("*a") log.debug("[PiTemplate]",string.format("raw freq %s",s)) s = string.match(s,"frequency%(..%)=(%d+)"); s = s/1000000 log.debug("[PiTemplate]",string.format("scaled freq %d",s)) properties.cpu_freq.value = s -- set property volts s = voltCmd:read("*a") log.debug("[PiTemplate]",string.format("raw volts %s", s)) s = string.match(s,"volt=(%d+\.%d+)"); log.debug("[PiTemplate]",string.format("scaled volts %.1f", s)) properties.cpu_volt.value = s end tasks.refreshProperties = function(me) log.trace("[PiTemplate]","~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In tasks.refreshProperties~~~~~~~~~~~~~ ") queryHardware() end Save the PiTemplate.lua file. cntrl x     Step 9: Run LSR   1. Navigate from installation directory to microserver directory. cd microserver 2. Ensure that wsems is running in a separate terminal session before you start running LuaScriptResource. 3. Ensure that you have ownership to the executable luaScriptResource and executable privileges. To check ownership: Ls -la -rwxrwxr-x 1 pi pi 769058 Jun 9 17:46 luaScriptResourc NOTE: The owner of luaScriptResource should be the user you are logged in as on the Raspberry Pi.   4. Confirm you have executable privileges by running the following command: sudo chmod 775 luaScriptResource 5. Run LuaScriptResource by executing the following command: sudo ./luaScriptResource   6. The output will show an error until we create the corresponding Thing in the next step.     Step 10: Bind Remote Thing Properties   Now we need to register a Thing so your Raspberry Pi can bind to its Properties on the Thingworx Platform.   Create a Thing named PiThing that will bind the scripts.PiThing created in config.lua . Open your Composer screen. Click Things on the left-navigation and the + symbol. Enter PiThing in the Name field and click RemoteThing in the Thing Template field.   Click Save. Ensure that the Remote Thing Property is connected. Click Properties in the left-hand navigation. Verify that the isConnected Property has a value of true. This means that your Raspberry Pi is still connected and now bound to this Thing on Thingworx Platform.   Bind the remote Thing Properties. Make sure the Properties tab is selected and click Edit at the top of the PiThing. Click Manage Bindings. Select the Remote tab at the top.   Click Add All Above Properties or drag and drop the ones you need. Click Done. Click Save. Verify that the Properties were updated with readings from the Raspberry Pi. Both the Value and Default Value for the three Properties will be set to the current reading from the Raspberry Pi. Cover the Raspberry Pi and wait about a minute, then Select the Properties tab and click Refresh. You will see the cpu_temperature value change.     NOTE: The system properties from your Raspberry Pi are now being passed to the server every 30 seconds. Wait a couple of cycles to see if the values from the Raspberry Pi change. If you are impatient, manually change the value of the property using the Set button in the Composer then click Refresh to see the updated value. The value will be temporarily updated for about 30 seconds until the Raspberry Pi reports the current live value.   Troubleshooting Tips   Tip #1 If the properties are not updating, try to stop and start both the wsems and luaScriptResource services.   quit sudo ./wsems or ./luaScriptResource Tip #2 If a wsems and/or luaScriptResource is not shut down gracefully, sometimes the service is still running which can cause issues. You can search and kill any wsems/luaScriptResource services by using the following command. Re-run the GetSystemProperties to test if this fixed the issue.   ps -efl kill -9 <id#>   Step 11: View Data from Devices   In order to demonstrate how ThingWorx can render a visualization of data from connected devices, at this point in the lesson you will import a pre-configured Mashup.   On the ThingWorx server that the EMS is connected to, start on the Home tab of Composer. Import a pre-built Mashup. Download and save the pre-built Mashup XML file attached here: Mashups_PiThingMashup-v91.xml. In Composer, click the Import/Export drop-down at the bottom-left.   Click Import. Leave all default values and click Browse to select the Mashups_PiThingMashup-v91.xml file that you just downloaded. Click Open, then Import, and once you see the success message, click Close. View Mashup displaying live data. Select the home icon in the top left side of Composer, then click Mashups on the left-navigation panel. Click Mashups_PiThingMashup-v91 and you'll see the design view of the Mashup.   Click View Mashup, and you'll see the live Mashup.    TIP: You will need to allow pop-ups in your browser for the Mashup to be displayed.     Click here to view Part 4 of this guide. 
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