IoT Tips

In this blog we will have a look at the installation of the Thingworx Analytics Builder extension. This is used as guideline but make sure to check the Help Center for your release as steps do vary with versions. The installation has been divided in 3 parts: Introduction and import of the extension into Thingworx Platform Video Link : 1568 Configuration of the extension Note: For release 8.1, the Settings menu differs from previous versions, seeWhat's New in ThingWorx Analytics Builder 8.1 between times 00:12 sec to 00:40 sec for up to date menu selection. Video Link : 1572 Installation of the UploadThing module Note: this step no longer applies as of ThingWorx Analytics 8.1 Video Link : 1573 Useful links: PTC Download page for Thingworx Analytics PTC Reference Document page for Thingworx Analytics How to copy files from Windows to Linux ?

Hi I have attached a Postman collection, this can be used as a template and be modified. steps to upload the collection to Postman. 1. In your Postman window click at Import. 2. Once you clicked import, you can chose your file. 3. The collection is now visible in your left side of the window.

Checkout the below video which explores the Creo As A Service (CAAS) feature of the Product Insight extension. This allows to retrieve Creo analysis computation inside ThingWorx through the Analytics Manager framework.  

With the advent of faster and cheaper hardware, and owing to vast improvements in connectivity such as Gigabit networks and 5G, data is collected and processed at an ever increasing pace. As such, there is a related need for the Analytical Models built on top of such data to evolve over time. As part of this evolution, modelling experiments with more data, new variables, new techniques, and different training parameters will be performed. The resulting models need to be tracked, monitored, and appropriate versions need to be used for scoring in various scenarios. This creates the need for version control of Analytical Models.   ThingWorx Analytics makes it easy to implement an “Analytics Model Version Control” system via two mechanisms: A job Id and timestamp based identification system, so if a model with the same jobName (model name) is retrained, the old model will still exist and can be easily retrieved A tagging mechanism for the above jobs   Before using the above techniques to version control Analytics Models, it is critical to decide what represents “the same model” to be versioned. Unlike in the world of Software Engineering where the concept to be versioned is a file / folder that evolves over time, in the world of Analytics Models, there could be several, potentially customer specific definitions. For example, one definition could be “same training parameters, but trained on the latest, most comprehensive data available”. Yet another, more relaxed definition could be “any training parameters, but same training dataset and goal” or even “any training parameters, on any historical version of the training dataset”.   Once this concept is agreed upon within the customer's organization, and if training is done in a ThingWorx service by calling the APIs, for a given jobName (model name) one can simply query the tags for a LatestVersion type tag, increment, and create the new model with the same jobName and incremented tag. Any model version with the same jobName and its corresponding performance metrics can then be accessed using the tag. Additional tags (such as techniques used, dataset version, etc) can be added if desired to make retrieval of context dependent models more efficient.

The intend of this post. This post is for the user who want to validate that, the ThingWorx Analytics Services related to Confidence Models work successfully. Underneath video walk through the steps to validate the Services via a non-supported PTC Mashup. The intend of this video is uniquely to validate that, Services related to Confidence Models works successfully.  What package files are used in the video? The Mashup entities and dataset used in the video, is attached to this post. Feel free to download the files and test on your machine. Why use Confidence Models? A confidence model is a way of adding confidence interval information to a predictive model. Statistically, for a given prediction, a confidence model provides an interval with upper and lower bounds, within which it is confident, up to a certain level, that the actual value occurs. During predictive scoring, this measure of confidence provides additional information about the accuracy of the prediction. More information about Confidence Models can be found here at PTC Help Center 

This demo walks through how Range Count works. The Range Count service calculates the difference between the maximum and minimum value.  Agenda of the demo: 1. Create a demo Thing 2. Add a new property to the Thing 3. Add the property statistical calculation type Range Count to the Thing 4. Validate the statistical calculation service via the added calculation type Range Count 5. Validate the statistical calculation service via the Service QueryTimedValuesForProperty      

Thingworx Analytics is offered through the User interface called Analytics Builder with some pre-configured functionality. However, should you want to create your own jobs and mashups, all features from Analytics Builder and some more are available through the Thingworx Services.  Running most functionality requires that you provide some data to run the Analytics Services. This is where the datasetRef parameter is required.        Data uploaded through Analytics Builder Any dataset uploaded through builder will require have a datasetUri as shown in the image above and format will be parquet (all small letters) datasetUri can be obtained from the list of datasets in builder Passing data as an in-body Dataset If data isn't uploaded through Analytics Builder, data can be supplied as an Infotable in the data parameter of the datasetRef. Metadata will also need to be supplied if a new dataset is being created (create Job of the AnalyticsServer_DataThing) If this data is being supplied for a scoring job, as long as the column names match up to what the model is expecting, TWX Analytics will inference them appropriately. The filter parameter is for parquet datasets already uploaded into TWXA and will take an ANSI SQL statement format to add conditions to reduce number of rows. Exclusions is an single column infotable list of the columns you wish to remove from the job you are trying to submit Example: If you want Profiles to only run on 5 out of 10 columns, you would give a list of 5 columns that you don't want to include in this exclusions infotable. Data may also be supplied as a csv file in the file repo in some cases, in which case you would give the dataseturi parameter the location of the file on the TWX File repo (of the format thingworx://UseCaseFileRepo/tempdata.csv) and the format which would be csv

When predicting a Boolean goal such as Failure in the next hour or any other goal that has a yes or no answer, Thingworx Analytics(TWXA) models will output a 'risk' of the event occurring. TWXA will intelligently pick a threshold beyond which that risk warrants attention. 1. In Analytics Builder, click on the export button 2. This will export a PMML model and download it for you 3. Open up the PMML model, in the output section, you will find a condition that explains the threshold that was selected by TWX Analytics.   In this example case, TWXA chose 0.5 as the best Threshold.   Note: The export button will only be available in Builder for TWXA 8.4+.

This video shows the steps to install ThingWorx Analytics Server 8.4 as well as the ThingWorx Analytics Extension.

Underneath video walks through how to Publish a Model from Analytics Builder into Analytics Manager using the connector named TW.AnalysisServices.AnalyticsServer.AnalyticsServerConnector.

One of the interesting features of ThingWorx Analytics Manager is its ability to run distributed models created in Excel (and more of course).  Most people having been tasked with understanding data have built models in Excel and have sometimes built quite complex models (or even applications) with it.   The ability to tie these models to real data coming from various systems connected through ThingWorx and operationalise their execution is a really simple way for people to leverage their existing work and I.P. on a connected analytics journey.   To demonstrate this power and ease of implementation, I created a sample data set with historical data, traffic profile, and a simple anomaly detection model to execute with Analytics Manager.  (files are attached)   The online help center was quite helpful in explaining the process of Creating the Excel Workbook, however I got stuck at the XML mapping stage.  The Analytics and Excel documentation both neglect to mention one important detail -- you must be using the Windows version of Excel in order to get the XML Source functionality (and I use Mac).  Once using Windows, it was easy to do - here is a video of the XML mapping part of the process (for the inputs and results).   

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. 

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 concludes Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It gives an overview of the "Statistical Process Control (SPC) Accelerator"

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 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 concludes Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. 

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 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.