IoT & Connectivity

Hello,I have Simulated Tags. The values from Thingworx are sent/written to the Simulated Tags. These simulated tags are used in Derived Tags to Calculate and send to Thingworx.But when KepServerEx is Re-Initialized, the simulated Tags become 0. Therefore, Derived Tag values are wrong. How can I prevent simulated Tags from becoming zero whenever KepServerEX is reinitialized? Thanks,

Hi there -We're on Thingworx 8.5.15.  I have a mashup that contains a dropdown widget.  I have a service that is used to populate the available choices for the dropdown.  I want to pass the selected values (plural) of the dropdown as a parameter to a second service for processing.  The second service takes a parameter of type String.  In this thread, @Ashritha states that it's a simple matter of binding the Selected Rows property of the first service as a parameter to the second service.  When I try this and log the parameter values received by the second service, I find that it only receives a single String value for processing. What must be done in order to pass multiple values to a service parameter and receive multiple values as a service parameter? thanks in advance,dgg

Hi All,I want to send mail to monitoring team in case of any alerts. So I want to know how to send mail from thingworx. Please let me know your inputs on the same. Unable to Invoke Service SendMessageWithNoReplyTo on EmailOnAFailureCase : Unable to send error email for logged incident

We have a mashup that is connected to Active Directory for authentication. When a user enters their log in credentials we have noticed that Security>Users is case sensitive. It will create a duplicate user name if the user fat fingers the shift key. For example: john.smith (actual log in name)John.Smith (Mistake)JOHN.SMITH (Mistake)When this occurs it prevents the user from accessing the mashup application until the duplicate user name is removed. Does Composer have a OOTB function that prevents this occurring or do we need to develop a custom solution to prevent this from occurring?

I have 2 tables called 'table123' and 'table246'.'table123' columns: 'ID', 'Dept_ID', 'First_Name', 'Surname', 'Salary', 'Address'.'table246' columns: 'Dept_ID', 'Dept_Name'.I'm looking for a list of employees with the lowest salaries in each department. I can accomplish it in two ways: an Equi-Join or an Inner-Join. I've been informed they can both be utilized to get the desired outcome. I used the following queries:Equi-Join:SELECT First_Name, b.Dept_Name, alt.Min_Salary AS Min_Salary FROM table123 a, table246 b, (SELECT Dept_ID, MIN(Salary)Min_Salary FROM table123 GROUP BY Dept_ID)alt WHERE a.Dept_ID = b.Dept_ID AND a.salary = alt.Min_Salary AND a.Dept_ID = alt.Dept_ID;Inner-Join:SELECT MIN(Salary)Min_Salary, Dept_Name FROM table123 a, table246 b INNER JOIN (SELECT First_Name, MIN(Salary) FROM table123 GROUP BY Dept_ID)alt ON b.Dept_ID = alt.Dept_ID;The Equi-Join command returns the appropriate table, with the columns 'First Name,' 'Dept Name,' and 'Min Salary' having all essential

HiAs the trial version of thingworx is not available on thingworx developer site. i want to install thingworx license for personal practice, how i will get that? is it affordable for personal practices? 

There is a situation where DataTables Data(Which is in Postgres) needed to be Migrated to the InfluxDB, such that going forward InfluxDb Streams will be used in DataTables. So, is there any best Practices by PTC provided ?@VladimirN @AnnaAn @paic @slangley @CarlesColl @c_lowy Thanks,Shashi. 

Hi Experts,   Please suggest a way to terminate user session from browser once the Logout is triggered. The user is being authenticated through SSO and want the user to click a button(probably logout) and should be redirected to login again to enter the Azure AD credentials instead of re-login automatically because the session is still alive.   I have already tried TerminateUserSession resource but that works only for Administrators group members and I have non-administrator custom user groups with specific permissions.   Note: Don't want to redirect to a different mashup/screen as a workaround.   TIA.

Migrated the Postgres DB from 10 to 13, but after the migration, data is not logging in to the ValueStream. Below is the Property Settings image. The DataTable entries are stored in DataTable Tables in DB, but not the Property values in the ValueStream Table. What would be the potential issue, or how can this issue be debugged?Thanks,Shashi.  

Placed an Line Chart on the Mashuip, But there is lot of spacing issue on the Chart, by which Dashboard Width and height needed to be increased. How can the spaces can be removed ?@c_lowy @slangley @VladimirN @CarlesColl Thanks,Shashi

This video is Module 10: ThingWorx Foundation & Analytics Integration of the ThingWorx Analytics Training videos. It gives a brief review of core ThingWorx Platform functionality, and how the Analytics server works on top of the platform. It also describes the process of creating a simple application, complete with a mashup to display the information from a predictive model. 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 available in the player menu so you can jump directly to each section. Chapter Summaries 0:00 – Intro: ThingWorx Foundation & Analytics Integration OverviewThis Module 10 introduction previews the ThingWorx framework, things with properties, services, events, and subscriptions, plus analytics communication methods and mashups for consumable insights. 0:33 – Things in ThingWorx: State, Behavior & Core ComponentsA thing is an instance of an object with state a

This video is Module 11: ThingWorx Analytics Mashup Exercise of the ThingWorx Analytics Training videos. It shows you how to create a ThingWorx project and populate it with entities that collectively comprise a functioning application.  It contains a recommended hands-on exercise. Here you are shown how to build a ThingWorx application that uses a ThingWorx Analytics predictive model. If your ThingWorx environment is connected to a streaming asset, say, through Kepware, you may use five tags from that asset rather than from the "CCPP" thing shown in this video. Simply bind to those tags and rename the new properties as shown in this video. NOTE: This exercise is only for demonstration purposes, and will not produce a meaningful or accurate model. **Update June 16, 2023: The following steps now reference "DataSimulator_Standalone.xml" instead of the previous "dataSimulator.xml". This new Thing will prevent errors on import.**If your environment isn't connected to a streaming asset, you

This video continues Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It begins with a ThingWatcher exercise, and concludes by describing Statistical Process Control (SPC). The "SPC Accelerator" will be covered in Module 9 Part 3. This video contains a hands-on exercise which you may complete as you wish. However, it's not necessary, as the following modules do not rely on the product of this exercise. It shows you how to configure a property on a thing for anomaly detection, and view the results in a mashup. Download and import the attached "Project_AnomalyDetection_ThingWatcher.xml". NOTE: It is recommended to import into a sandbox or non-production environment running ThingWorx/ThingWorx Analytics 9.2 or later. Chapters are available in this video — use the chapter menu in the player to jump to any section.Chapter Summaries 0:00 – Intro: Thing Watcher Anomaly Detection Demo with a SimulatorKicks off a hands-on walkthrough of configuring ThingWorx Thing Watcher

This video begins Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It describes how Thingwatcher can be set up to monitor values streaming from connected assets, and send an alert if its behavior deviates from its 'normal' behavior. 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 Module 9 – Anomaly Detection, Thing Watcher & SPCKicks off Module 9 with a roadmap covering the ThingWorx Analytics Thing Watcher feature, a hands-on alert configuration exercise, statistical process control (SPC) concepts, and how the SPC accelerator jump-starts building an SPC application. 0:32 – ThingWorx Analytics Server: Builder, Manager & Codeless Model BuildingExplains that the ThingWorx Analytics Server powers predictive signals, profiles, machine learning

This video concludes Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It gives an overview of the "Statistical Process Control (SPC) Accelerator" This video doesn't contain a required hands-on exercise. If you are interested in the accelerator, feel free to explore the accelerator by following along with this video. A team at PTC built the accelerator to expedite the process of building an SPC application inside ThingWorx. This accelerator is not a fully-developed or supported PTC product - it is a set of ThingWorx entities for the user to modify and employ as needed. If interested, download the attached "Statistical Process Control Documentation_v6.5.pdf" and "SPC 6.5 Import Package.zip". NOTE: It is recommended to import into a sandbox or non-production environment running ThingWorx 9.1.6 or later.  This video has chapters — open the chapter menu in the player to jump to any section. Chapter Summaries 0:00 – Introduction: SPC Accelerator in ThingWorx & SPC

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 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 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 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 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 8: Time Series Modeling of the ThingWorx Analytics Training videos.  It contains a hands-on exercise, and it is recommended to complete it before moving on to subsequent sections. In this exercise you are shown how to train a time series model and interpret the performance metrics. Download and use the attached "gas_prices.csv", This video has chapters — use the chapter menu in the player to jump to any section. Chapter Summaries0:00 – Getting Started: Navigate to the ThingWorx Analytics Data SectionKicks off the walkthrough by opening ThingWorx Analytics and using the left-side Analytics icon to reach the Data area where datasets are created and managed. 0:16 – Create a Dataset: Upload US Gas Prices CSV & Enable Time Series DataShows how to create a new dataset (named "US Gas Prices"), attach the source CSV with Choose File, uncheck the upload-metadata option, tick the Time Series Data box, and Submit so ThingWorx Analytics infers the metadata. 0:55 – C

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 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 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 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