IoT & Connectivity

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 contains a hands-on exercise, and it is recommended to complete it before moving on to subsequent sections. This exercise involves training a predictive model using ThingWorx Analytics dataset created in Module 4. NOTE: If you did not yet create a dataset, the process is reviewed in this video.This video has chapters — use the menu in the player to jump to any section Chapter Summaries: 0:00 – Neural Network Models: Hidden Layers, Adaptive Weights & Binary ClassificationExplains how neural networks use interconnected input, hidden, and output nodes with adaptive weights to approximate functions, handle n

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 contains a hands-on exercise, and it is recommended to complete it before moving on to subsequent sections. This exercise involves using the ThingWorx Analytics dataset created in Module 4 to run a signals job.Note: This video has chapters — use the menu in the video player to jump to any section. Chapter Summaries 0:01 – Introduction: Module 5 Descriptive Analytics in ThingWorx Analytics This opening frames Module Five and previews how to perform descriptive analytics in ThingWorx Analytics using signals, profiles, and clustering to derive relations and patterns from your data. 0:28 – ThingWorx Analytics Data Flow: Feature Engineering to Champion Model Walks through the analytics-ready pipeline — raw source data, data profiling, feature engineering, and me

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 does not contain any hands-on exercises, and you don't need access to a running ThingWorx Analytics environment. This video has chapters — use the chapter menu in the player to jump to any section. Chapter Summaries 0:00 – Accuracy vs. Recall: Metrics for Imbalanced ClassesExplains why accuracy misleads on imbalanced data using a healthcare example where only 0.02% of patients develop kidney disease, showing a 99.9%-accurate model can be useless and recall is the better performance metric. 1:17 – Upsampling / Oversampling: ThingWorx Analytics for Rare EventsCovers upsampling (oversampling) to duplicate and saturate positive observations so machine learning algorithms learn rare events, and how ThingWorx

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 does not contain any hands-on exercises, and you don't need access to a running ThingWorx Analytics environment. This video has chapters — use the chapter menu in the video player to jump straight to any section. Chapter Summaries 0:00 – Process Framework: ThingWorx Analytics Overview & the Three Analytics QuestionsThis intro maps the end-to-end ThingWorx Analytics process framework as a logical step-by-step progression, framing the three guiding questions: why something happened, what will happen, and what you can do to change the outcome. 0:18 – Descriptive Analytics: Historical Data, Key Factors & Pattern DetectionExplains descriptive analytics as the most basic analytics type, condensing big data into simpler information (similar to Google Analytics) to i

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. This video does not contain any hands-on exercises, and you don't need access to a running ThingWorx Analytics environment. This video has chapters — use the chapter menu in the player to jump to any section. Chapter Summaries 0:00 – Introduction: ThingWorx Analytics Training Modules Overview Kicks off the ThingWorx Analytics training course, previewing the upcoming modules and hands-on exercises, and introducing Module 1 as the platform and analytics overview. 0:19 – Mirror Worlds: Physical-Digital Convergence & the IoT Vision Explains David Gelernter's early-1990s "Mirror Worlds" concept from Yale, showing how connected sensors, cameras, and ubiquitous wireless networks converge the real and digital worlds into smart systems. 1:52 – PTC Strategy: CAD, PLM, IoT & the AR D

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 does not contain any hands-on exercises, and you don't need access to a running ThingWorx Analytics environment. This video has chapters — use the chapter menu in the player to jump to any section. Chapter Summaries 0:00 – Module 3 Intro: Data Profiling, Metadata & BeanPro DatasetThis module introduces data profiling in ThingWorx Analytics, explaining how it assesses a data source's suitability for an analytics use case, what metadata is, and how to profile the sample BeanPro dataset used throughout the course. 0:30 – ThingWorx Analytics Data Flow: Raw Data to Champion ModelWalks through the end-to-end ThingWorx Analytics pipeline: raw CSV, streaming IoT, or data-warehouse sources are structured into a contextual repository and dimensional model, then combined wi

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 contains a hands-on exercise, and it is recommended to complete it before moving on to subsequent sections. This exercise involves taking an analytics ready view in .csv format, and using it to create a ThingWorx Analytics dataset. Download and use the attached "BEAN_PRO_DATA_PROCESSED.csv", or use the file you modified in Module 3 Part 3.This video has chapters — open the chapter menu in the player to jump to any section. Chapter Summaries 0:00 – Module 4 Intro: Data Transformation & Feature Engineering OverviewIntroduces Module Four and its goals: defining data transformation and feature engineering for ma

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 does not contain any hands-on exercises, and you don't need access to a running ThingWorx Analytics environment. This video has chapters — open the chapter menu in the video player to jump to any section. Chapter Summaries 0:00 – Introduction: ThingWorx Analytics Use Cases with Matt Hardman (PTC)Matt Hardman, who leads PTC's AI and analytics in-market team, introduces the session on real-world ThingWorx Analytics customer deployments and the process for building sustainable, production-ready predictive analytics use cases. 0:36 – Framing the Use Case: "Given / Predict / Optimize" Business Problem MethodExplains the "Given [data], I want to understand/predict/optimize [outcome] so that [action]" pattern, using examples like predicting electric

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 does not contain any hands-on exercises, and you don't need access to a running ThingWorx Analytics environment. This video has chapters — use the chapter menu in the player to jump straight to any section below. Chapter Summaries 0:00 – Data Profiling & Metadata Basics: JSON Format for ThingWorx AnalyticsExplains how data profiling helps create or validate metadata—data that describes a ThingWorx Analytics data set—which must accompany every loaded data set and is defined using a JSON file format. 0:30 – Field Name & Values Parameters: Ordinal and Categorical RequirementsCovers the required field name that must exactly match the source feature, plus the values parameter listing possible entries

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 contains hands-on exercises, and it is recommended to complete them before moving on to subsequent sections. Download and use the attached "BEAN_PRO_DATA_UNPROCESSED.csv" for the exercises. To compare your work against a processed version of the data, see "BEAN_PRO_DATA_PROCESSED.csv"This video has chapters — use the chapter menu in the player to jump to any section. Chapter Summaries 0:00 – Introduction: BeanPro Espresso Machine Dataset & Data Profiling Results Introduces the data profiling results for BeanPro, a fictitious espresso machine manufacturer investigating machin

ThingWorx Monitoring and Alerting, Part 2Using Prometheus and GrafanaBy Tori Firewind, IoT EDCBuilding Dashboards    To add a panel which monitors some component of the ThingWorx application to a dashboard in Grafana, click to add a new panel. Under “Metrics” in the box at the bottom of the screen, select what ThingWorx metrics you wish to monitor (type “thingworx” in the search box to see them all). For example, select the Platform Subsystem memory in use:    Label filters aren’t necessary, though you may want to sort by instance if you are monitoring multiple ones with the same dashboard. You may also want to take some time to format the Y axis, which by default will show in bytes. Go to the formatting panel on the right side and scroll down to the section called “Standard options”. For the Unit dropdown, start typing “data” and then select “bytes (SI)”. This will automatically determine if the bytes you’ve provided should really print as MB or GB based on how lar

ThingWorx Monitoring and Alerting, Part 1Using Prometheus and GrafanaBy Tori Firewind, IoT EDCIntroduction and Getting Started    As ThingWorx has become a more mature product during the lifetime of the IoT EDC, so too have our dev ops recommendations. As we’ve stated throughout many posts now, testing is a key part of ensuring enterprise readiness, and it occurs at every stage of the process: from unit testing to preserve individual service logic, to integration tests which preserve the functionality of the application as a whole, to user and edge load testing and user experience testing, which ensure enterprise readiness. So testing is a critical component, but the process of dev ops never stops. In order to effectively test the system, a comprehensive monitoring solution is also required.    Once the application is tested and the changes pushed into production, there is no knowing with certainty that everything will run smoothly indefinitely. Random spikes in usa

  We have to upgrade Thingworx 8.5.1 to 9.3.x. Tools: Thingworx Installer - 8.5.1  Java - jdk1.8.0_171 PostgreSQL - 10   Trying to simulate in test environment. While installing Thingworx 8.5.1 getting following error,   Logs:   [2022-12-23T02:44:22-08:00] INFO: windows_service[ThingWorx-Foundation] configured. [2022-12-23T02:45:23-08:00] ERROR: Running exception handlers [2022-12-23T02:45:23-08:00] ERROR: Exception handlers complete [2022-12-23T02:45:23-08:00] FATAL: Stacktrace dumped to c:/progra~2/thingw~1.1/installer/foundation/local-mode-cache/cache/chef-stacktrace.out [2022-12-23T02:45:23-08:00] FATAL: Please provide the contents of the stacktrace.out file if you file a bug report [2022-12-23T02:45:23-08:00] FATAL: Chef::Exceptions::MultipleFailures: Multiple failures occurred: * Timeout::Error occurred in delayed notification: Timeout::Error * Timeout::Error occurred in delayed notification: Timeout::Error    Attached logs from C:\Program

I'm trying to get Thingworx to issue a schedule to restart a client PC. Is this possible either through the client or as a Javascript set of commands?Thanks

Hi, I do have a few Entities tagged with the tag Applications: Sprint1; how can I get all the Entities under that Tag in a Service output to have an Info table?Thanks,

Hey guys,     I have list of documents(link) coming from windchil and want to download all at once to a folder and need to zip it.   suggestion please     Thanks  

Good day community, I have a connected device represented by a thing. There is a property on the thing called: last connection. I am representing this thing using a label and an icon on the mashup side. This icon should change to a green color when the device is connected and then switch to a red color when the device is offline. How do I go about writing a dynamic service to accomplish this?Many thanks,  

Hi Developers,I'd like to get your support on CSS part.I'm try to hide the disclosure button in the value display widget with custom css, however it doesnt work.Could you tell me how we can hide the button ?our thingworx env is "ThingWorx 9.2.6-b366". 

After installing the Thingworx Analytics Extension, the Analytics tab is not showing up in Composer although the Analytics Server Things do show up and are 'connected'.Details:We have a Thingworx Foundation cluster (3 nodes) running Thingworx 9.3.0 on RHEL.We just finished installing Thingworx Analytics Server 9.3.0 on a separate RHEL node.  As part of the Server installation, we set it to install the Analytics Extension in Foundation.  It gave us a message that the extension was queued for install on the next platform restart. Since it's a cluster, we weren't sure whether that meant to restart just one of the nodes, all of them individually, or all of them at the same time.  So we tried all three of these methods.  We also tried installing the extension directly in Composer.  The Analytics tab for Analytics Builder/Manager still doesn't show up in Composer.  Any ideas on what we may have missed?

The following videos are provided to help users get started with ThingWorx:     ThingWorx Installation ThingWorx PostgreSQL Setup for Windows ThingWorx PostgreSQL for RHEL   ThingWorx Data Storage Introduction to Streams Introduction to Value Streams Introduction to DataTables Introduction to InfoTables     ThingWorx Concepts & Functionality Introduction to Media Entities Using State Formatting in a Mashup Configuring Properties     ThingWorx REST API REST API (Part 1) REST API (Part 2)     ThingWorx Edge SDK Configuring File Transfer with the .NET SDK     ThingWorx Analytics Getting Started with ThingWorx Analytics Part 1 Getting Started with ThingWorx Analytics Part 2 Installing ThingWorx Analytics Builder Part 1 of 3 Installing ThingWorx Analytics Builder Part 2 of 3 In

Is there any way to send multiple outputs from the single service at a set of intervals?  for ex:  I have an HTML snippet to display images using the HTML text area widget. In my case, I have a list of image URLs in an array to display. My intention after invoking the service user should see all the images, each image should stay on the screen for 2 seconds.  unfortunately, the HTMLTextArea widget is not taking a script from the HTML code.

var query2 = {"filters":{"type": "OR","filters": [{"type": "NE",fieldName: "name",value:"ConsolidatedInfoFiles"},{"type": "NE",fieldName: "name",value:"Health Summary"},{"type": "NE",fieldName: "name",value:"Shiftwise Melt Consumption"},{"type": "NE",fieldName: "name",value:"Shiftwise Energy Consumption"}]}};var params1 = {t: infoIT /* INFOTABLE */,query: query2 /* QUERY */};var result = Resources["InfoTableFunctions"].Query(params1);

Hi Experts,   I have a very specific requirement to highlight certain bars in a Bar Chart without changing original colors of the bars.   Current scenario: Data infotable is bound to Chart and chart adjusts and displays bars according to the data value with different color for all bars.   Expected: Data infotable has 2 values beyond threshold and chart should highlight those 2 bars in red color and rest of the bars should remain in original(different) colors.   Tried and didn't work: Applied state formatting with a threshold value which turns all the bars meeting condition in red color and rest of them in another single color. Attached is the screenshot of the same.     Example: Display top 2 fruits bars in red which has highest sales i.e. Banana and Pear.    

I need to read the SciptLog file from Thingworx to check the activities of a particular process. Is it possible to read the ScriptLog content from Thingworx ?ThanksDimitri

Good day community, I am making use of In range alerts to change the values associated with my alerts dynamically. Below is my code. It executes successfully.  However, the values on my alert do not change. Does anyone know why this is happening? var params = {infoTableName: "InfoTable",dataShapeName: "NumberRangeAlert"/* DATASHAPENAME */};var table = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape(params); table.minimum = Min;table.minimumInclusive = minInclusive;table.maximum = max;table.maximumInclusive = maxInclusive;table.calculationValue = 1;table.calculationType = "Percentage";table.calculationShow=true;table.propertyValue=13;let thingProperties = Things[selectedEntity].GetPropertyDefinitions({category: 'X' /* STRING */,type: 'Number' /* BASETYPENAME */,dataShape: "NumberRangeAlert"/* DATASHAPENAME */});for(let i =0; i<thingProperties.length; i++){let name = thingProperties[i].name;let alertName = "MyAlert1";var params1 = {alertType: "InRange"