IoT Tips

This video begins Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. It describes the differences between time series and cross-sectional datasets. It begins to show how ThingWorx Analytics automatically transforms time series datasets into ones that are ready for machine learning. 

This video concludes Module 7: Predictive & Prescriptive Scoring of the ThingWorx Analytics Training videos. It describes how ThingWorx Analytics automatically evaluates a range of values for chosen fields to produce prescriptive scores. 

This video begins Module 7: Predictive & Prescriptive Scoring of the ThingWorx Analytics Training videos. It describes how a trained machine learning model takes inputs and makes predictions of different kinds, depending on the use case. It shows how scoring works in production, taking inputs from various sources and producing a score to help users make informed decisions. It also covers the concept of field importance in an individual score.

This video concludes Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It covers some modeling techniques to help build better predictive models. It discusses the dangers of models that overfit data, and how to avoid overfitting. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It then begins to describe some of the performance metrics used to evaluate predictive models. 

This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It describes the remaining machine learning algorithms used by ThingWorx Analytics to build predictive models that weren't covered in Part 1. In addition, this video describes the different kinds of ensembles you can build that utilize multiple algorithms. 

This video begins Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It gives examples of different types of goal variables. It also discusses data considerations in predictive modeling, It begins describing the machine learning algorithms used by ThingWorx Analytics to build predictive models. 

This video concludes Module 5: Descriptive Analytics of the ThingWorx Analytics Training videos. It covers signals, profiles, and clusters, and how these forms of descriptive analytics provide crucial insight into your data.

This video begins Module 5: Descriptive Analytics of the ThingWorx Analytics Training videos. It covers signals, profiles, and clusters, and how these forms of descriptive analytics provide crucial insight into your data.

This video concludes Module 4: Data Transformation & Feature Engineering of the ThingWorx Analytics Training videos. It covers Descriptive Services and Derived Properties, and how they can be leveraged to create helpful alerts and make data transformation easier. 

This video begins Module 4: Data Transformation & Feature Engineering of the ThingWorx Analytics Training videos. It describes what data transformation is, and how feature engineering can improve machine learning models. You will learn about independent and dependent variables in your data, and how an "analytics ready view" looks for use with ThingWorx Analytics.

This video concludes Module 3: Data Profiling of the ThingWorx Analytics Training videos. It shows you a few examples of questions that should be asked of a subject-matter expert (SME) to better understand the information contained in a dataset. Using answers to these questions, you will  use a tool such as Microsoft Excel to modify a given dataset, and prepare it for future exercises in this course.

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

This video begins Module 3: Data Profiling of the ThingWorx Analytics Training videos. It describes the process of examining your data to make sure that it is suitable for the use case you would like to explore.

This video is Module 2: Use Case Discussion of the ThingWorx Analytics Training videos. It covers what a use case is, and what a successful use case requires. It details a few examples that have been explored using ThingWorx Analytics. 

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

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

  Connect   Connect Your Data to ThingWorx 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. Your decision on which connectivity method to pick will be dependent on your individual use case.   Learning Paths Connect and Configure Industrial Devices and Systems   Featured Guides Install ThingWorx Kepware Server Connect to an Azure OPC UA Server   REST API Use the REST API to Connect Low-Capability Devices to ThingWorx   Using the ThingWorx REST API is an easy way for low-capability devices to connect with the ThingWorx platform and push data to the platform. Any edge device that can make an HTTP POST can read and update properties or execute services on the ThingWorx platform.   Choose a Connectivity Method Use REST API to Access ThingWorx Connect an Arduino Developer Board   Edge SDKs Connect natively to ThingWorx using an AlwaysOn protocol SDK.  Secure, embeddable, and easily deployable communications designed for connecting sensors, devices and equipment across any network topology and any communication scenario.   SDKs are available for Java, C, .net and allow you to connect your devices to ThingWorx with the AlwaysOn protocol. Using the Edge SDKs will give you all the flexibility you need to meet your application's requirements and build robust, secure, full-featured edge integrations and gateways for any platform.   ThingWorx Edge SDKs SDK Reference C SDK Tutorial Java SDK Tutorial   Edge Microserver The Edge Microserver proxies connections via AlwaysOn   Connect your devices to the ThingWorx platform with the Edge MicroServer, a pre-built application that enables devices incapable of making TLS connections to securely interact with the platform.   Connect Raspberry Pi to ThingWorx Choose a Connectivity Method   Kepware Server Access data from industrial machine controllers   ThingWorx Kepware Server with 150+ industrial protocol drivers allows you to easily connect to different types of industrial equipment. The interface provides real-time, bi-directional industrial controls data to the ThingWorx Platform via the AlwaysOn protocol.   Install ThingWorx Kepware Server   Device Cloud Connectors Connect devices with the adapter of your choice and integrate with ThingWorx to build scalable IoT applications.   Connect Azure IoT Devices     Analyze   Analyze and Visualize IoT Data The AI and Machine Learning technologies used in ThingWorx Analytics automate much of the complex analytical processes involved in creating data-driven insights for your IIoT application. Simulate behavior of physical products in the digital world, use predictive analytic algorithms to find patterns in your business data and generate a prediction model, or build a real-time anomaly detection model by monitoring for data points that fall outside of an expected range.   Learning Paths Monitor Factory Supplies and Consumables Design and Implement Data Models to Enable Predictive Analytics   Featured Guides Operationalize an Analytics Model Build a Predictive Analytics Model   Perform Analytical Calculations Embed analytics capabilities into your industrial IoT applications in order to monitor real-time data, predict future events and conditions, and optimize performance of devices and organizations.   Operationalize an Analytics Model Build a Predictive Analytics Model Monitor an SMT Assembly Line Statistical Monitoring with Descriptive Analytics Perform Statistical Calculations with Descriptive Analytics     Build   Rapid, Model-based Application Development Build your industrial IoT application using ThingWorx’s drag-and-drop GUI development environment, model-based development platform. Using the ThingModel to describe assets, processes, and organizational elements and how they relate to each other. Define the functional behavior, add business logic, and extend your application with pre-built plugins. With a properly-constructed framework, your application will be scalable, flexible and more secure.   Learning Paths Medical Device Service Design and Implement Data Models to Enable Predictive Analytics   Featured Guides Get Started with ThingWorx for IoT Data Model Introduction   Build the Data Model Define the properties, services, and events of Things you want to expose to your application developers. The ThingWorx Data Model is a logical representation of the physical devices, systems, and people that interact with your application.   Data Model Introduction Monitor an SMT Assembly Line Data Model Implementation Design Your Data Model   Leverage the Data Model Leverage your data model using events subscriptions, and custom business logic.   Monitor an SMT Assembly Line Methods for Data Storage Bind Data to Widgets Implement Services, Events, and Subscriptions Create Custom Business Logic Application Development Tips & Tricks Create Session Parameters   Extend the Platform Capabilities Take advantage of extensions from partners and third-parties to add new functionality into your system in a seamless manner. Extensions can be service (function/method) libraries, connector templates, widgets, and more.   Create An Extension Create A Mashup Widget Extension Create An Authentication Extension     Manage   ThingWorx Platform Management Efficiently manage your assets with visibility and control over your IoT solution. Install, configure and troubleshoot your application, while monitoring performance and communication with devices. Offering a comprehensive set of tools and features, ThingWorx enables remote access, file transfers, software upgrades, logging, debugging, and more.   Learning Paths Getting Started on the ThingWorx Platform Using an Allen-Bradley PLC with ThingWorx   Featured Guides Deploy an Application   Manage Your Platform Compare Persistence Providers   Manage Your Applications Operationalize application updates, OS upgrades, patches and documentation.   Deploy an Application Compare Persistence Providers     Experience   Design Engaging Experiences Use the industry’s first purpose built IoT application development environment to design engaging experiences for web and mobile applications. Designed to reduce the time, cost, and risk required to build new innovative IoT applications, this layer has two distinct functions: build-time and run-time. Build-time encompasses the technology to create the things in your Industrial IoT solution while Run-time includes the operational permissions to execute and manage those things.   Learning Paths Getting Started on the ThingWorx Platform Customize UI and Display Options to Deploy Applications   Features Guides Create Your Application UI   Application Layout (UI)  Utilize the ThingWorx Mashup Builder tools to design and create engaging IoT applications.   Define Your UI Style Add Style to Your UI with CSS Effective UI Implementation   Charts & Graphs Bring your IoT data to life with dynamic charts and graphs.   How to Display Data in Charts   Reusable Components Leverage the ThingWorx widget library to create a robust user experience and enhance your application capabilities.   Object-Oriented UI Design Tips Display Geolocation Data Using Google Maps Organize Your UI with the Collection Widget     Secure   Securely Collect and Process Data ThingWorx is secure by design and offers multiple authentication options to increase the security of your IoT application. From TLS-encrypted communication and role-based access controls to the distribution of security patches, ThingWorx integrates a range of security features that you can leverage in your development process.   Learning Paths Getting Started on the ThingWorx Platform   Featured Guides Configure Permissions   IoT Application Security Authenticate devices on our platform. ThingWorx handles data transformation, data persistence, and business logic so you can focus on developing your application.   Configure Permissions Enabling LDAP Authentication in ThingWorx Create An Authentication Extension Create An Application Key

    Step 4: Use Collection Widget   At this point, you have created the following Entities:   A Thing with data stored in an Info Table Property (columns defined by a Data Shape)   A base Mashup (containing Gauge, LED Display, and Text Field Widgets) that displays an individual row of data from the table   Now, you need to use the Collection Widget to display this Mashup for every row of data in the table.   On the ThingWorx Composer Browse tab, click Visualization > Mashups, + New              2. Keep the default of Responsive with no Templates chosen, and click OK.        3. In the Name field, enter cwht_collection_mashup              4. If Project is not already set, search for and select PTCDefaultProject.        5, Click Save.        6. Click Design               7.  On the top-left Layout tab, ensure that the default of Positioning > Responsive is selected.  You want the "base" Mashup to generally be Static, while the "collection" Mashup should be Responsive to allow it to grow to an appropriate size.         8. From the top-left Widgets tab, drag-and-drop a Collection Widget onto the central Canvas area.       Access Mashup Data Service   Click the + icon at the top-right to open the Add Data pop-up menu.               2. In the Entity Filter field, search for and select cwht_thing.         3. In the Services Filter field, enter getproperties.         4. Click the right arrow beside GetProperties to add the GetProperties Service to the right-side of the pop-up menu.         5. Click the Execute on Load checkbox             6. Click Done to close the pop-up menu. The GetProperties Service of the cwht_thing is now shown on the right under the Data tab.         7. On the top-right, expand GetProperties.           8. Drag-and-drop GetProperties > infotable_property onto the Collection Widget            9. On the Select Binding Target pop-up, click Data     Configure Properties   In the Collection Widget's Filter Properties field in the bottom-left, enter mashup. This will limit the displayed Properties of the Collection Widget (in the bottom-left section) to only those containing the word "mashup".            2.  For the Mashup Property, search for and select cwht_base_mashup.  Choosing "cwht_base_mashup" lets the Collection Widget know which base Mashup to repeat in every Cell               3. In the MashupHeight field, enter 250 and hit the Tab keyboard key to apply the change. This Property changes the height of each Cell of the Collection Widget. Ensure the height is large enough to fit all of the Widgets.          4. In the MashupWidth field, enter 500 and hit the Tab keyboard key.  This Property changes the width of each Cell of the Collection Widget. Ensure the is large enough to fit all of the Widgets.            5. In the Filter Properties field, enter uidfield.          6. In the UIDField drop-down, select first_number.  The UIDField sets the unique identifier for the Collection Widget.            7. In the Filter Properties field, enter sort.          8. In the SortField drop-down, select first_number.  SortField determines which sub-section of the Infotable to use for sorting purposes.          9. Check the SortAscending checkbox            10. n the Filter Properties field, enter mashupprop           11. Click Add, copy-and-paste the following JSON into the MashupPropertyBinding Property, replacing the existing curly braces {}, then click Done.   The point of this JSON is to relate the columns of the Infotable Property to the Mashup Parameters that we previously defined in the base Mashup. { "first_number" : "first_number", "second_number" : "second_number", "third_number" : "third_number" }                12. Click Save, then View Mashup.     Notice that the base Mashup we previously created has been replicated multiple times, corresponding to each row of the Info Table Property.   As an extension exercise, go back into cwht_thing and add another row of data. Refresh the Collection Widget Mashup to see another instantiation of the base Mashup automatically added to display that new row.   A Mashup utilizing the Collection Widget dynamically expands to accommodate both the user and the available data.   Step 5: Next Steps   Congratulations! You've successfully completed the Organize Your UI guide, and learned how to: Create a Datashape to define columns of a table Create a Thing with an Info Table Property Create a base Mashup to display data Utilize a Collection Widget to display data from multiple rows of a table Learn More   We recommend the following resources to continue your learning experience: Capability Guide Build Application Development Tips & Tricks Experience ThingWorx Application Development Reference   Additional Resources   If you have questions, issues, or need additional information, refer to:   Resource  Link Community Developer Community Forum Support Collection Widget Help Center  

    Select the right database to use with your ThingWorx installation.   Guide Concept   When you develop an application with ThingWorx, you must save the configuration data that defines the data model and the user interface. You also need to store the dynamic data that is generated by devices at runtime (such as temperature or location).   ThingWorx uses the term Persistence Provider to refer to any type of service that saves application data, usually it is a database.   When your application moves into production you must choose and configure a Persistence Provider that meets your requirements.     You'll learn how to   Pros and cons of different databases that can be used as persistence providers for ThingWorx The database best suited to certain applications Where to find detailed information about the Persistence Providers   NOTE: The estimated time to complete this guide is 30 minutes       Step 1: Persistence Provider Options   There are many factors that will influence your decision for which Persistence Provider to use with your ThingWorx instance. On this page we compare and contrast different methods and provide examples for where each one is a natural fit.    Persistence Provider             Typical Use H2 Bundled with ThingWorx server for proof-of-concept trials PostgreSQL Standard production database for use up to 15,000 Property writes per second InfluxDB High volume data ingestion (>25,000 property writes per second) into one database Microsoft SQL Server SQL database available from Microsoft in a dozen different editions Azure SQL Server SQL database managed by Microsoft in Azure   When ThingWorx is installed with default configurations, it uses the embedded H2 database as its Persistence Provider. This configuration is suitable for evaluations and proof-of-concept applications with a limited number of Things. Before an application is used in production, you must provision a more capable persistence provider. The available options for Persistence Provider are summarized below.   H2   Pros                            Cons                     Typical Use Case                                DBA Skills       Required Cost No Database set-up required Not for use in production Start testing ThingWorx with no additional configuration None No additional cost   H2 is an open source, full-featured relational database that is embedded in the ThingWorx Foundation server. No additional database set-up is required before developing a proof-of-concept application with ThingWorx. Using H2 should provide satisfactory performance for applications with less than 1000 Things.   WARNING: Due to the inability to back-up and recover the database, H2 should never be used in production.   PostgreSQL    Pros                    Cons                        Typical Use Case                     DBA Skills               Required Cost Widely used database Requires some configuration Workhorse database appropriate for many applications Basic SQL skills No additional cost   PostgreSQL is the default choice for ThingWorx cloud hosting servers. It complies with many database standards and is open source with no extra license fee required. It can be configured for high availability to minimize the chance of down-time or data loss. It has been tested up to 15,000 property writes per second and depending on other factors may give satisfactory performance up to 25,000 writes per second.   Learn more about using PostgreSQL:   Using PostgreSQL as the Persistence Provider ThingWorx PostgreSQL Administrator's Guide   InfluxDB   Pros                      Cons                            Typical Use Case            DBA Skills Required           License Cost Handle high volume data ingestion InfluxDB is not supported as a Property provider Application requiring >25,000 Property writes/second Professional services likely required Multi-node requires license fee   If your system intensively deals with time series data and your implementation heavily depends on Value Streams or Streams for persistence/retrieval of data, we recommend using InfluxDB as the Persistence Provider in ThingWorx. InfluxDB is a high-performance data store written specifically for time series data and is a good choice when high volume data ingestion of more than 25,000 property writes per second must be saved into one database.   Learn more about InfluxDB:   Using InfluxDB as the Persistence Provider   Microsoft SQL Server   Pros                     Cons                       Typical Use Case                    DBA Skills                      Required Cost Available in multiple editions Only runs on Windows Data stored in Microsoft SQL Server Configure settings License fee required   More than a dozen different versions of Microsoft SQL Server are used by customers for workloads ranging from small single-machine applications to large enterprise applications. Connecting ThingWorx to an existing Microsoft SQL Server can make that data readily available to use in ThingWorx Mashups.   Learn more about using Microsoft SQL Server:   Using Microsoft SQL Server as the Persistence Provider Getting Started with MS SQL and ThingWorx   Azure SQL Server   Pros                                    Cons                        Typical Use Case              DBA Skills         Required Cost Cloud deployment and scaling No on-premise option Data stored in Microsoft SQL Server Configure settings Subscription required   Fully managed database service operated and updated by Microsoft Learn more about using Microsoft SQL Server:   Using Azure SQL Database as the Persistence Provider   Step 2: Next Steps   Congratulations! You've successfully completed the Compare Persistence Providers guide. At this point, you can make an educated decision regarding which Persistence Provider is best suited for your ThingWorx application development environment.   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 Microsoft SQL Technical Support Support Persistence Provider Help Center