The IoT Enterprise Deployment Center’s goal is to create and share knowledge around the best practices for architecting, designing, and deploying successful, enterprise-scale Thingworx IoT Solutions.    To accomplish this goal, the EDC team takes a “real world” approach, using simulated IoT assets and users to benchmark the capabilities of different Thingworx deployment configurations. First, each implementation is pushed to its limit in an effort to establish real-world baselines, metrics which can be used to help customers determine which architecture choices will work for their custom needs. Then, each implementation is pushed beyond its limits, providing useful insight into where and why things fail, and illuminating potential implementation changes which could push the boundaries further.   Through the simulations testing to come, the EDC will be publishing the resulting benchmarks for all to see! These benchmarks will include details on implementation goals and performance metrics for different stages of deployment. Additionally, best-practice articles which illustrate how to deploy the different architectural components (those referenced within the benchmarks) will also be posted, highlighting the optimal approach to integrating everything into the Thingworx platform.   Stay tuned to see more about just how versatile the ThingWorx Platform can be! We look forward to discussing these findings as they are published right here on the PTC Community.  ... View more

Please find here an Labview implementation to connect to Thingworx via RestCalls. Have Fun using it. Any Feedback is appreciated. https://github.com/Seppel1985/LabVIEW_TWX_RestAPI ... View more

The below video shows the steps to install ThingWorx Platform and ThingWorx Analytics trial edition. The trial editions can be downloaded from the Developer Zone at https://developer.thingworx.com/resources/downloads . You can then follow the guides at https://developer.thingworx.com/resources/guides to get familiar with ThingWorx and ThingWorx Analytics.       ... View more

PTC introduces a next-generation technical support service - called Proactive Support Services - that allows customers to proactively manage their PTC products and be notified of risks before they become problems. PTC’s Proactive Support Services, available to customers now, increases user productivity, reduces the impact of security risks and lowers overall administrative effort.   “This is an incredibly valuable service. Windchill Method Server logs can be overwhelming to review and analyze. The idea of proactively reviewing logs and providing insight is a huge benefit and value for Windchill System Administrators,” commented Tom Sisler, Engineering Services, Knoll, Inc., who participated in the beta program. “We generally find ourselves reacting to events. This service will likely prevent issues, and as a result improve the entire lifecycle of Windchill operation and management. Thank you!”   The On-Demand System Scan service allows system administrators to manually scan their system files for similar recommended improvements on an ad hoc basis. Admins can upload system files to be scanned and receive recommended actions for any known issues or improvements that are detected.   The Proactive Monitoring & Guidance service combines always-on system monitoring that pairs deep scanning capabilities with solutions and insights to help customers get the most out of their PTC software. The recommendations engine offers a wide range of proactive stability, performance, and security improvements through configurable, prescriptive suggested solutions.   You can find out more about PTC’s Proactive Support Services by visiting the FAQ page.   ... View more

How to score new data with ThingWorx Analytics ?   The following is valid starting with ThingWorx Analytics (TWA) 8.3.0   Overview   Once a training model has been created, one of the main objective is to score new data to predict the value for the goal ThingWorx Analytics can score new data in 2 ways: Batch scoring Real time scoring Batch scoring   Batch scoring will be used when a large amount of data needs to be scored. To perform a batch scoring we will usually follow steps similar to the below ones: Upload the historic data Create a new model with this historic data Upload new data – the one to be scored Perform a prediction job to score those new data Retrieve the prediction job result Uploading the new data can be done in different ways. If using a large amount of data, it can be easier to upload the data via a csv file in a similar way as the historic data. This is the way used in ThingWorx Analytics Builder. If the amount of data is more limited this can be sent in the body of the scoring request. The post  Analytics: Prediction Methods Mashup   shows a good example of how to do this using the PredictionThing.BatchScore service. We are focusing below on ThingWorx Analytics Builder, that is uploading new data via a csv file. In order to perform the scoring job only on the new data in step 4 above, we need to be able to filter those added data. If the dataset has already suitable column/feature such as a timestamp for example, we can use this to score only new data after timestamp > newdate, assuming all data are in chronological order. If the dataset has no such feature, we will have to add one  beforehand when we first upload the historic data in step 1 above. We often use a new column/feature named record_purpose to this effect. So initial data can take a value of training for this record_purpose feature since they are used to create the initial model. Then new added data to be scored can get any value that identify those rows only. It is important to note that this record_purpose feature needs to be set with the optType INFORMATIONAL so as to not be taken into account by the learning algorithms.   The video below shows those steps while using ThingWorx Analytics Builder   Real time scoring   Real time scoring is better suited for small amount of data. The process for real time scoring can be done either via the Analytics Server PredictionThing RealTimeScore service or using the Analytics Manager framework. The posts How to work with ordinal and categorical data in ThingWorx Analytics  and Analytics: Prediction Methods Mashup  do give  examples of the use of the RealTimeScore service.   We will concentrate below on the Analytics Manager. The process involves the following steps: In Analytics Manager Create an Analysis Provider that uses the AnalyticsServerConnector connector Publish the model created in ThingWorx Analytics Builder to Analytics Manager Enable the model created Create an Analysis Event Map the properties to the datashape field Enable the Event In ThingWorx Composer Relevant properties of the Thing used in the Analysis Event are updated in someway This trigger the analysis job to be executed The scoring result is populated into the result property mapped in the Analysis event The Help Center has got more detailed about this process. The following video shows those steps Following articles can also be of interest for this topic: How to use ThingPredictor in release 8.3 of ThingWorx Analytics Server ? Publish model from Analytics Builder into Analytics Manager using TW.AnalysisServices.AnalyticsServer.AnalyticsServerConnector Creating Template For Thing, And Configure Analysis Event For Real-Time Scoring via Analytics Manager Note that the AnalyticsServerConnector connector in release 8.3 replaces the ThingPredictor connector from previous releases. ... View more