IoT & Connectivity Tips

This video continues Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. It continues to show how ThingWorx Analytics automatically transforms time series datasets into ones that are ready for machine learning. It also describes the concept of virtual sensors. It finishes by describing the time series dataset that will be used in the following modules. This video prepares for a hands-on exercise that will be shown in a following module, In this exercise you will be shown how to train a time series model. Download and use the attached "gas_prices.csv",This video includes chapters. Use the chapter menu in the player to jump directly to specific topics. Chapter Summaries: 0:00 – Time Series Prediction: History Pivoting & Temporal FeaturesThingWorx Analytics transforms time series data into a non-time-series format through history pivoting, incorporating past feature values, goal values, and engineered temporal metrics such as averages, derivatives, and volatilit

This video concludes Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos.  This video contains a hands-on exercise, and it is recommended to complete it before moving on to subsequent sections. This exercise shows you how to train a categorical model, and get predictive scores from it using the Analytics microservices. Download and use the attached "iris_data.csv".Note: This video has chapters — use the chapter menu in the player to jump to any section.Chapter Summaries 0:01 – Create ThingWorx Analytics Dataset: Iris CSV UploadWalks through the Analytics > Data area to create a new ThingWorx Analytics dataset named "Iris Data," uploading the Iris CSV while unchecking the upload-metadata and time-series options before submitting. 0:51 – Reviewing Inferred Metadata & Dataset DetailsShows how ThingWorx Analytics auto-infers metadata — sepal/petal length and width as DOUBLE/continuous and class as STRING/categorical — then confirms the datas

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 contains a hands-on exercise, and it is recommended to complete it before moving on to subsequent sections. In this exercise you will use the model you trained in Module 6 to perform a batch scoring job. This video has chapters — jump to any section using the chapter menu in the video player. Chapter Summaries 0:00 – Module 7 Overview: ThingWorx Analytics Data-to-Insight PipelineIntroduces Module Seven on predictive and prescriptive scoring, explaining how ThingWorx Analytics ingests raw source data from social, client, text file, s

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 contains a couple hands-on exercises, and it is recommended to complete them before moving on to subsequent sections. In one exercise you will use the model you trained in Module 6 to perform a batch prescriptive scoring job. In the other you will export a predictive model from your ThingWorx Analytics environment as a .pmml file, and then import it back in.This video has chapters — use the chapter menu in the player to jump to any section.Chapter Summaries: 0:00 – Real-Time Scoring: ThingWorx Analytics Prediction Thing ServiceIntroduces the RealTimeScore service on the Analytics Server prediction thing for fast, single-record scoring, contrasts it with batch scoring for large datasets, and shows how it binds into custom ThingWorx Found

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 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 8 Intro: Time Series in ThingWorx AnalyticsAn overview of Module 8, introducing time series data, how it differs from cross-sectional data, and the time series functionality available inside ThingWorx Analytics. 0:22 – Cross-Sectional Data: Order-Independent Features & ExamplesDefines cross-sectional data as a snapshot from many subjects at a single point in time, with many features and order-independent rows that are ready for analytics immed

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 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 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 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 0:01 – Descriptive Services Intro: ThingWorx Analytics 8.3 Feature EngineeringIntroduces descriptive services, added in ThingWorx Analytics 8.3, which engineer meaningful features from time-series data without custom code, powered by the Descriptive Analytics microserver that must be running to enable both new microservers. 0:33 – Statistical Calculation Microserver: IoT Time-Series AggregationCovers the Statistical Calculation microserver and its library of services that aggregate and transform raw

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 contains a hands-on exercise, and it is recommended to complete it before moving on to subsequent sections. This exercise of training a predictive model is continued from Part 2.This video has chapters — use the chapter menu in the video player to jump to any section. Chapter Summaries 0:00 – Advanced Model Configuration: Validation Holdout Percentage & Max FieldsWalks through the Advanced Model Configuration tab in ThingWorx Analytics, explaining the default 20% Validation Holdout (the 80/20 training-validation split) and lowering Max Fields from 25 to 15 of 34 fields to reduce overfitting while preserving predictive power. 0:58 – Redundancy Filter & Confidence Model AvailabilityCovers the Redundancy Filter option, which keeps only top signals with high mutu

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 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 6 Overview: Predictive Models & Model Validation in ThingWorx AnalyticsThis introduction outlines what the module covers: predictive analytics fundamentals, predictive modeling techniques, model training, setting options and parameters in ThingWorx Analytics, and model accuracy, performance, and advanced modeling techniques. 0:32 – Data Foundation: Source Data, Contextual Repository & Feature EngineeringExp

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 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 profiles job. This video has chapters — use the chapter menu in the player to jump straight to any section. Chapter Summaries 0:00 – Profiles: Introduction & Definition in ThingWorx Analytics Introduces profiles as a descriptive analytics method in ThingWorx Analytics, defining a profile as a statistically distinct subpopulation and explaining how it identifies data hotspots for groups that over- or underperform relative to the goal variable. 0:38 – Profiles Business Value: Machine Failure Hotspot Example Illustrates profiles with a machine-failure scenari

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 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 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 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 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 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 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

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

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

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

ConnectConnect Your Data to ThingWorxLearning PathsFeatured GuidesREST APIEdge SDKsEdge MicroserverKepware ServerDevice Cloud ConnectorsAnalyzeAnalyze and Visualize IoT DataLearning PathsFeatured GuidesPerform Analytical CalculationsBuildRapid, Model-based Application DevelopmentLearning PathsFeatured GuidesBuild the Data ModelLeverage the Data ModelExtend the Platform CapabilitiesManageThingWorx Platform ManagementLearning PathsFeatured GuidesManage Your PlatformManage Your ApplicationsExperienceDesign Engaging ExperiencesLearning PathsFeatures GuidesApplication Layout (UI)Charts & GraphsReusable ComponentsSecureSecurely Collect and Process DataLearning PathsFeatured GuidesIoT Application Security Connect Connect Your Data to ThingWorxIn 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 provid

Pre-built apps for manufacturing operations that rapidly deliver value. Overview Think Big, Start Small, Scale Fast Getting started on an industrial IoT project can be daunting, especially deciding where and how to begin. After collecting the experiences gained by working with over 1500 companies on IIoT applications, PTC has made the process to get up and running easier. By grouping IIoT capabilities together in functionally oriented programs, PTC built out-of-the-box applications that rapidly deploy and scale across new or existing system infrastructure. Instead of starting from scratch, you can use ThingWorx ready-to-configure apps to quickly lay the foundation for industrial transformation and implement IIoT solutions in as little as 90 days. ThingWorx Apps offer a comprehensive, basic IIoT scheme to connect to your equipment, collect real-time data, create notification work flows, deliver role based dashboards, and more. Need something more tailored to your operatio

Learning Paths combine guides into one experience to teach related skills efficiently from start to finish. Begin your learning journey to reduce time to proficiency to get up and running with ThingWorx quickly. Featured Learning Utilizing ThingWorx to Secure Your Aerospace and Defense SystemsConnect and Monitor Industrial Plant EquipmentVehicle Predictive Pre-Failure Detection with ThingWorx Platform Industry Solutions Complex and Automatic Food and Beverage SystemsConnect and Configure Industrial Devices and SystemsMedical Device ServiceMonitor Factory Supplies and ConsumablesUsing an Allen-Bradley PLC with ThingWorx Learn ThingWorx Getting Started on the ThingWorx PlatformDesign and Implement Data Models to Enable Predictive AnalyticsCustomize UI and Display Options to Deploy ApplicationsAzure MXChip Development Kit