IoT Tips

Underneath, video is about ThingWorx Analytics and walks through following functions: Upload a Dataset. Create a Training Model. Create a Scoring Job.

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

Continuing our series of Troubleshooting ThingWorx Analytics installations, in this IoT Tech Tip we will cover two items have been appearing for many users.   Error 1069 Encountered with Native Windows Installation of ThingWorx Analytics 8.2   In some instances, when a user successfully installs ThingWorx Analytics (TWAS) to a Windows Server operating system, they will encounter an error where TWAS will report an Error 1069: The Service did not start due to logon failure.   This can occur with any individual Service that is created by the installation, the following fix should work in addressing the issue.   Primary Reason This Happens:   This error can be encountered when the user provides incorrect credentials for associating the Services to an account during installation. In TWAS 8.2, there is a utility that will enable to the user to change the associated user on the Services. It is important the user provides the password for the User Account on Windows, and not the user/password combination for ThingWorx Foundation Platform Server.   Steps to Fix Issue   Solution 1:   Open a Command Prompt as Administrator, via Start Menu à Run à type CMD. Then right click on cmd.exe and Run As Administrator.   In the elevated command prompt, change your directory to the ThingWorxAnalyticsServer/bin directory, for example in the default installation path would be: cd C:\Program Files (x86)/ThingWorxAnalyticsServer/bin Then execute the changeServiceUserAccount.bat <username>, for example: changeServiceUserAccount.bat user1   You will be prompted to change the password for the user.   Solution 2:   If Solution 1 does not resolve the issue, alternately you can manually change the Log On properties for each of the services. The changeServiceUserAccount.bat would do this via script, but on occasion this may work. Open the Control Panel and navigate to Services, for example: Control Panel à All Control Panel Items à Administrative Tools   You will have to right click each individual service and go to Properties à Log On tab and enter the account name and password for the local account. Note: Local System account will not resolve this issue.   This issue was resolved in the ThingWorx Analytics Server 8.3 release, where all Services are associated with the Network Service account.     More information can be found in this Knowledge Article   Uploading of a Dataset hangs or does not complete in ThingWorx Analytics 8.3   On occasion, after a fresh installation of ThingWorx Analytics Server 8.3 on a Windows Server operating system, a dataset will not complete its upload. Typically no error message is displayed, and the upload wizard UI will just hang on the upload progress after:   Creating copy of Configuration File... Submitting Create Dataset request... Creating copy of Data File...   Primary Reason This Happens:   This is caused by twas-zookeeper service being stuck in a PAUSED state. This means that in the post installation, twas-zookeeper did not start.   Steps to Fix Issue   You will have to double check that the JAVA_HOME variable was defined as a System Variable. In the ThingWorx Analytics Installation guide, pages 12-14 outline the steps required as pre-requisites. You can change this in Control Panel > System > Advanced Settings > Environment Variables, and ass a new variable named JAVA_HOME under System Variables. The value location should be the location of your deployment of JAVA software.   Typically this is located in C:\Program Files\Java\<jre or jdk>_<version number>     More information can be found in this Knowledge Article

Precision and Recall are the evaluation matrices that are used to evaluate the machine learning algorithm used. This post needs some prior understanding of the confusion matrix and would recommend you to go through it here.   Example of Animal Image Recognition Consider the below Confusion Matrix for the input of the animal images and algorithm trying to identify the animal correctly: ANIMALS Cat Dog Leopard Tiger Jaguar Puma Cat 62 2 0 0 1 0 Dog 1 50 1 0 4 0 Leopard 0 2 98 4 0 0 Tiger 0 0 10 78 2 0 Jaguar 0 1 8 0 46 0 Puma 2 0 0 1 1 42   Explaining Few Random Grids: [Cat, Cat]: The grid is having the value 62. It means the image of a cat was identified as a cat for 62 times. [Cat, Dog]: The grid is having the value 2. It means the image of a cat was identified as dog twice. [Leopard, Tiger]: The grid is having the value 4. It means the image of Leopard was identified as Tiger for 4 times.   Questions To Find Some Answers    Q.How many times our algorithm predicted the image to be Tiger? A. Looking at the Tiger column: 0+0+4+78+0+1 = 83   Q.What is the probability that Puma will be classified correctly? A. Looking at the matrix above, we can see that we 42 times Puma was classified correctly. But twice it was classified as Cat, once as Tiger and once as Jaguar. So the probability will come down as: 42/(42+2+1+1)= 42/46 = 0.91      This concept is called as RECALL. It is the fraction of correctly predicted positives out of all actual positives. So we can say that Recall = (True Positives) / (True Positives + False Negatives)   Q. What is the probability that when our algorithm is identifying the image as Cat, it is actually Cat? A. Looking at the matrix above, we can see that once our algorithm has identified a Dog as a Cat, twice Puma as Cat and 62 times Cat as Cat. So the probability will come down as: 62/(62+1+2) = 0.95      This concept is called as PRECISION. It is the fraction if correctly predicted positives out of all predicted positives. So we can say that Precision = (True Positives) / (True Positives + False Positives)

This video gives an introduction to the Descriptive Services: what they are how to install them how to configure them how to use them  

This video shows the steps to install ThingWorx Analytics release 8.3  

ThingWorx 8.3 covers the following areas of the product portfolio:  ThingWorx Analytics, ThingWorx Utilities, and ThingWorx Foundation which includes Connection Server and Edge capabilities.   Highlights of the release include:   ThingWorx Foundation Next Generation Composer: Now default admin and developer interface Full Feature parity with legacy Composer New capability for User and Group administration, Authorization and permissions, Export, Monitoring and Logging. More in Helpcenter Localization support for German and French Mashup Builder: JQuery 3 upgrade Grid Advanced Extension now supports Cell Editing and Footers Platform: Active Directory (AD) Integration enhancements for larger AD forests and user extension field mapping Upgrade in-place enhancements for Java SDK developers Developer Enablement Capture the usage statics such as time taken to execute a ThingWorx service, # of times a service runs in ThingWorx using Service Utilization Statistics functionality powered by all new and efficient Utilization Subsystem. Collect ThingWorx system data such as ESAPI configuration, ThingworxStorage logs, licensing, and JVM information to better diagnose system issues Service Utilization Statistics: ThingWorx Support Package tool Administrator Password and Password Length New installations of ThingWorx will be required to supply the initial Administrator password of the installer’s choice. That password must be supplied via a new entry in the platform-settings.json file. After the initial installation, the Administrator password should then be changed to a strong password to be used going forward. Additional information. As a step toward industry best practices, the Administrator password and all new passwords will need to be at least 10 characters.  When upgrading to 8.3, passwords from older versions of the platform will not need to be modified, but any new passwords being created will need to be at least 10 characters long. See the installation instructions for complete details.   ThingWorx Analytics New Descriptive Services  Core statistics (min, max, deviation, etc.), data distribution (binning), confidence intervals, and other useful calculations. Frequency analysis and transformation (via fast Fourier transform) for troubleshooting use cases and predictive analytics applications Improves users’ ability to apply logic and derive the following insights from streaming data without constructing complex models or accessing machine learning: Enables platform developers to easily process platform data in their applications and prepare the data for predictions. Statistical Process Control (SPC) Services Provides industry-standard calculations that allow IoT developers to implement SPC “control chart rules” in their applications.  Useful in manufacturing and in monitoring equipment and processes. Supports a wide assortment of rules, including number of points continuously above / below a range, in and out of range, increasing or decreasing trends, or alternating directions. Analytics Workbench Bundles the two Analytics interfaces (Analytics Builder and Manager) into a new Analytics section in Composer. Predictive Analytics Improvements Reduces overall install and administration complexity. Improves handling of time dseries data when used in predictive scoring. Includes a new learner, Support Vector Machines, enhancing the platform’s utility in building Boolean predictions. Includes a new ensemble method, Majority Vote, that improves generated model accuracy. Provides redundancy filtering which can optionally remove redundant information to improve explanatory analytics (Signals) and predictive model training. Now supports time series lookahead configuration, simplifying this type of prediction. Replaces ThingPredictor predictive scoring in Analytics Manager with native Analytics Server scoring: Improves scalability of concurrent jobs. Axeda Compatibility Package IDM Connector Support o   ACP v1.1.0 introduces the IDM Connector which enables Axeda customers to connect their Axeda IDM agents to the ThingWorx platform.  The IDM Connector provides support for registration requests, property updates, faults, events, file uploads and downloads.  Axeda ThingWorx Entity Exporter Update o   ACP v1.1.0 also includes an updated version of Axeda-ThingWorx Entity Exporter (ATEE) which now supports exporting Axeda IDM assets from the Axeda application into a format that can be imported in the ThingWorx Platform.  eMessage Connector Improvements o   Additionally, ACP v1.1.0 includes support for instruction based Software Content Management packages for the eMessage Connector which allows you to download file(s), execute instruction(s) and optionally restart the agent.  The Axeda Compatibility Extension (ACE) has new entities to support the IDM Connector and SCM for the eMesssage Connector.  o   Finally, updated versions of the Axeda Compatibility Extensions (ACE) and the Connection Services Extension (CSE) are included in ACP v1.1.0 and provide an improved workflow for granting permissions to the eMessage and IDM Connectors. ThingWorx Extension Updates Websocket Tunnel Extension Update The Websocket Tunnel Extension was updated for 8.3 to support the upgrade to jQuery3 Grid Advanced 4.0.0 comes with 2 key features: Editing - we now have cell editing support for all basetypes. The previous version had boolean editing; 4.0.0 now includes support for all basetypes. Footers - A footer section can now be added to the Grid to display rolled-up Grid totals. You can perform client-side calculations like count, min, max and average, and it includes support for custom functions. Note - Grid Advanced 4.0.0 only supports ThingWorx 8.3 and above. Custom Charts 3.0.1 12 Bug Fixes Google Maps 3.0.1 General Bug Fixes ThingWorx Utilities With the 8.3 Release, ThingWorx Utilities functionality are being repackaged into ThingWorx Foundation and ThingWorx Asset Advisor.  ThingWorx Workflow will now be available with Foundation.  The functionality from the Asset and Alert Management Utilities will be delivered in ThingWorx Asset Advisor.  ThingWorx Software Content Management capabilities will continue to be available for customer to manage the delivery of Software to their Connected Products.  The naming of “Utilities” is being phased out of the ThingWorx Platform packaging but the key functionality formerly described as ThingWorx Utilities continues to be delivered with version 8.3.   ThingWorx 8.3 Reference Documents ThingWorx Analytics 8.3 Reference Documents ThingWorx Platform 8.3 Release Notes ThingWorx Platform Help Center ThingWorx Edge SDKs and WebSocket-based Edge MicroServer Help Center ThingWorx Connection Services Help Center ThingWorx Analytics Help Center ThingWorx Industrial Connectivity Help Center ThingWorx Utilities Help Center ThingWorx Utilities Installation Guide     ThingWorx eSupport Portal ThingWorx Developer Portal PTC Marketplace   The following items will be available for download from the PTC Software Download site on June 8, 2018. ThingWorx Platform – Select Release 8.3 ThingWorx Utilities – Select Release 8.3 ThingWorx Analytics – Select Release 8.3 ThingWorx Extensions – Select Individual Extensions for download.  Will be available with the next Marketplace refresh

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).   

In this IoT-AR Tech Tip, we will cover what the microservices do, and their functions overall in the ThingWorx Analytics Server Application.   With the new architecture changes introduced with ThingWorx Analytics 8.1, many users have asked for a more descriptive explanation of the purpose of the microservices that make up the Analytics Server application. In 8.3, Descriptive Microservice has been introduced, and changings in how Predictive Scoring were incorporated. ThingPredictor has been deprecated and its primary functions have been incorporated into ThingWorx Analytics Server’s Prediction microservice.   Many of these microservices can be installed as separate distinct utilities, though there some that are required for base functionality of Analytics Server. This allows custom installation by the end-user to tailor the TWA deployment to their needs.   ThingWorx Analytics Server Microservices   Analytics Microservice – Analytics Server Edge Agent acts as an integration point between the ThingWorx server and the ThingWorx Analytics server. The Edge Agent automatically registers with ThingWorx and instantiates the Things that represent the connected ThingWorx Analytics microservicers. This component is mandatory.   Clustering Microservice - The Clustering Microserver provides services that categorize dataset records into groups (clusters) based on their similarities. Clustering returns results in the form of a PMML model. This component is optional.   Data Microservice - The Data Microserver provides data-handling services. These services include creating a dataset, appending new data, viewing dataset metadata, querying and retrieving information about the distribution of data in the dataset. This component is mandatory.   Predictive Microservice - The Prediction Microserver provides both real time and asynchronous (batch) predictive scoring. The scoring process evaluates each record in a dataset against a prediction model. Each record is assigned a predictive score that reflects the model's predicted outcome for that record. This component is optional. This component replaces ThingPredictor in functionality.   Prescriptive Microservice - The Prescriptive Microserver provides real time prescriptive scoring, which examines how certain changes might affect future outcomes. Fields identified as levers can be varied to determine how specific changes might affect future outcomes. This component is optional.   Profiles Microservice - The Profiling Microserver provides services to identify distinct subpopulations (profiles) within a dataset that share similar characteristics and are different from other subpopulations in statistically significant ways.  Profiles are not required in order to make a prediction, but they contribute to a strategic understanding of the complex factors associated with specific outcomes. This component is optional.   Results Microservice – The Results Microserver provides services for working with results. It also provides services to query input and output fields for Training and Clustering results, which output in PMML format. This component is mandatory.   Training Microservice – The Training Microserver uses machine learning algorithms and techniques to identify meaningful patterns in a dataset and generate a predictive model. A number of parameters are available for tuning the type and combination of learning techniques used. The resulting model is output in PMML format. This component is optional, however, if the Validation Microserver is selected, the Training Microserver will be selected by default.   Validation Microservice – The Validation Microserver provides metrics to evaluate how well a model was able to predict outcomes for a specific goal variable in a dataset. Depending on the goal variable, validation metrics can include RMSE, Matthew's Correlation (MCC), Pearson Correlation, true and false positive rates, accuracy, or a confusion matrix. This component is optional, however if the Training Microserver is selected, the Validation Microserver will be delected by default.   Analytics Worker – The Analytics Worker does the processing work in the system. It picks up jobs from the Zookeeper system and performs the requested work. By default, only one worker is installed, but the number of workers can be scaled up after installation. Workers can process any type of submitted job request. This component is mandatory.   ThingWorx Analytics – Descriptive Analytics Microservice   Descriptive Analytics Microservice is an optional microservice included with the ThingWorx Analytics Server installation package, but can operate as a distinct and separate utility.   Descriptive Analytics provides a library of on-demand services that perform common statistical calculations and facilitate statistical monitoring on raw data. Descriptive Analytics is not required to generate prediction models, but output from these services can provide insights that improve your understanding of your data.   Update January 6, 2020   For information about ThingWorx Analytics 8.5 and newer, please refer to the HelpCenter - Analytics Server System Architecture for 8.5

This video walks through the dataset requirements when working with time series in ThingWorx Analytics Server.  Starting release 52.2 - ending release 8.2.   Also view: - Written version of those steps mentioned in the video - Help Center  

This video go through the steps required to use the Creo Insight extension: - Download and install the required extension - Set required config.pro options - Create provider in Analytics Manager - Publish sensor from Creo - Create analysis Event in Analysis Manager - Retrieve sensor values from ThingWorx in Creo     See also: - https://www.ptc.com/en/support/article?n=CS277514  for a  written version of those steps. - Creo Help Center  

Thing Subscription This post is intended for novice ThingWorx users who wants to understand what the definition of Thing Subscription is and the overall purpose of using Thing Subscriptions.   Definition of a Thing Subscription? A Thing subscription is a script(JavaScript) that is called each time an event occurs. Events are property states which are of end users interest (e.g. temperature) and therefore indicators to kick off some functionality in a Thing subscription when any action needed. Events can e.g. be triggered by an Alert that detects a change or an anomaly in property values. The Thing subscription is explicitly linked to an event and when the event is fired the data is being passed to the subscriber.    Why Use a Thing Subscription? Imagine your machine is running 24 hours 7 days a week with supervised human interaction. If a pump temperature exceeds accepted value it needs to be regulated by the manufacturing department. But no one in the department knows when the temperature will exceed accepted value or drop suddenly therefore, the machines is always sporadically physically supervised by humans which leads to heavy costs for the manufacture. With a Thing Subscription a notification alert email can be sent directly to the department manager who acts based on the email notification.   Thing Subscription must have A Thing subscription must have defined a rule which gets executed when an event occurs. The definition of the rule may accommodate any appropriate business logic.   Thing Subscription example process In this scenario Thing subscription is using a predictive analytics model to detect Data Change or any anomaly values going through a Thing Property. So, based on historical data including failure information, a predictive analytics model begins to analyze run-time values from individual Things/properties to the analytics server. The predictive analytics model detects a pattern which detects past failures, when the analytics model predicts a failure/event based on the analyzed patterns an action is being fired via a Thing subscription. That action could be for ThingWorx to create a service ticket or send a notification email to the service department.   Example of a simple Thing Subscription set-up without using Analytics model to analyze data but instead a build-in ThingWorx alert Below example of Thing Subscription will send a notification email when temperature exceeds defined values from ThingWorx alert configuration. Prerequisites; it is necessary to have a mail server extension imported into the ThingWorx Composer this enables the service department to receive the email notification when an event have occurred. The extension can be downloaded from the marketplace. 1. Create a Thing with the MailServer[i] as the Base Thing template.     2. Create a new Thing and add Properties together with an alert that is triggered when the value exeeds user defined temerature.   3. Enable the Thing Subcriptions by Select Subscription and click +Add Make sure to mark the checkbox Enabled Selecting your Event name and your Property name In the right side of the screen you can enter your script/function that will notify ThingWorx email service to create the email notification Select Done and Save   4. Enable Email notification by selecting Services Provide an name Select Me/Entities Mark Other entity Find your Thing where the MailServer is the Thing Template   5. Then find the SendMessage snippet/script and fill out the snippet with your personal information.   [i] View this blog for more information on how to install the MailServer

Datasets with ordinal or categorical goal cannot currently be used in ThingWorx Analytics Builder. However this is only a UI limitation, ThingWorx Analytics Server can handle those data. It does simply require to use the services from the AnalyticsServer-Training and AnalyticsServer-Prediction things to perform the operations.   This can be done using a mashup or via Rest API call (see https://www.ptc.com/en/support/article?n=CS271485 ) . The below video expands on the mashup solution. Attached are also the entities used during the video and a sample dataset with ordinal goal.     Update for ThingWorx 9.0  The API has changed in 9.0, use the entities Entities-90-3Jun2020.xml for release 9.0  

Predictive models: ​ Predictive model is one of the best technique to perform predictive analytics. This is the development of models that are trained on historical data and make predictions on new data. These models are built in order to analyse the current data records in combination with some historical data.   Use of Predictive Analytics in Thingworx Analytics and How to Access Predictive Analysis Functionality via Thingworx Analytics   Bias and variance are the two components of imprecision in predictive models. Bias in predictive models is a measure of model rigidity and inflexibility, and means that your model is not capturing all the signal it could from the data. Bias is also known as under-fitting.  Variance on the other hand is a measure of model inconsistency, high variance models tend to perform very well on some data points and really bad on others. This is also known as over-fitting and means that your model is too flexible for the amount of training data you have and ends up picking up noise in addition to the signal.   If your model is performing really well on the training set, but much poorer on the hold-out set, then it’s suffering from high variance. On the other hand if your model is performing poorly on both training and test data sets, it is suffering from high bias.   Techniques to improve:   Add more data: Having more data is always a good idea. It allows the “data to tell for itself,” instead of relying on assumptions and weak correlations. Presence of more data results in better and accurate models. The question is when we should ask for more data? We cannot quantify more data. It depends on the problem you are working on and the algorithm you are implementing, example when we work with time series data, we should look for at least one-year data, And whenever you are dealing with neural network algorithms, you are advised to get more data for training otherwise model won’t generalize.  Feature Engineering: Adding new feature decreases bias on the expense of variance of the model. New features can help algorithms to explain variance of the model in more effective way. When we do hypothesis generation, there should be enough time spent on features required for the model. Then we should create those features from existing data sets. Feature Selection: This is one of the most important aspects of predictive modelling. It is always advisable to choose important features in the model and build the model again only with important and significant features. e. let’s say we have 100 variables. There will be variables which drive most of the variance of a model. If we just select the number of features only on p-value basis, then we may still have more than 50 variables. In that case, you should look for other measures like contribution of individual variable to the model. If 90% variance of the model is explained by only 15 variables then only choose those 15 variables in the final model. Multiple Algorithms: Hitting at the right machine learning algorithm is the ideal approach to achieve higher accuracy. Some algorithms are better suited to a particular type of data sets than others. Hence, we should apply all relevant models and check the performance. Algorithm Tuning: We know that machine learning algorithms are driven by parameters. These parameters majorly influence the outcome of learning process. The objective of parameter tuning is to find the optimum value for each parameter to improve the accuracy of the model. To tune these parameters, you must have a good understanding of these meaning and their individual impact on model. You can repeat this process with a number of well performing models. For example: In random forest, we have various parameters like max_features, number_trees, random_state, oob_score and others. Intuitive optimization of these parameter values will result in better and more accurate models. Cross Validation: Cross Validation is one of the most important concepts in data modeling. It says, try to leave a sample on which you do not train the model and test the model on this sample before finalizing the model. This method helps us to achieve more generalized relationships. Ensemble Methods: This is the most common approach found majorly in winning solutions of Data science competitions. This technique simply combines the result of multiple weak models and produce better results. This can be achieved through many ways.  Bagging: It uses several versions of the same model trained on slightly different samples of the training data to reduce variance without any noticeable effect on bias. Bagging could be computationally intensive esp. in terms of memory. Boosting: is a slightly more complicated concept and relies on training several models successively each trying to learn from the errors of the models preceding it. Boosting decreases bias and hardly affects variance.     

Alerts via Anomaly Detection This documents objective is to provide information and links about alerts used for anomaly detection. This document covers following topics: What Is Anomaly Detection Implementing Anomaly Detection Creating an Anomaly Alert and Prerequisites Anomaly Stats Certainty Parameter Video Example On How To Create An Alert for Anomaly Detection Tips and troubleshooting What Is Anomaly Detection Anomaly Detection in ThingWorx is implemented via built-in ThingWatcher functionality. ThingWatcher detects anomalies by monitoring a data stream from a device, calculating an expected distribution of data, and validating that the current data point is a member of the expected distribution.   Implementing Anomaly Detection Anomaly Detection is enabled by default in ThingWorx. However, several steps are required to configure the functionality for your specific environment, including the prerequisite activities below.   Creating an Anomaly Alert and Prerequisites   Configuring Anomaly Detection to monitor a stream of data. For information about setting up Anomaly Detection, view Preparing ThingWorx for Anomaly Detection. Anomaly Stats Anomaly Alert Statuses moves through several statuses as it works its way through the corresponding phases. Initialized Calibrating Training Buffering Monitoring Failed Certainty Parameter The Certainty Parameter when implementing anomaly detection requires a number of factors to consider. At its most basic, ThingWatcher functionality compares two sets of data, a validation set (collected during the Calibrating phase) and a test dataset (data streaming from a remote device). ThingWatcher tries to determine the likelihood that the distribution of values in the test dataset is from the same distribution of values contained in the validation dataset. The accuracy of the model plays a large role in this determination, but so does the Certainty parameter used for the statistical analysis of the two data sets.   Video Example On How To Create An Alert for Anomaly Detection Anomaly Detection Part 1. Create connectivity between KEPServer and ThingWorx Platform. Anomaly Detection Part 2. Configure Anomaly Alert to bind simulated data coming through KEPServer for Anomaly Detection. Anomaly Detection Part 3. Viewing data via Anomaly Mashup. Tips and troubleshooting Diagnose and fix the most common issues that may be encountered when working with ThingWatcher. It cannot be stressed strongly enough that you should be familiar with your data including the average time interval between data points, and the collection duration and certainty threshold you specified.

Video Author:                     Asia Garrouj Original Post Date:            March 31, 2017 Applicable Releases:        ThingWorx Analytics 7.4 to 8.1   Description: This video is the second part of a two part video series walking thru the configuration of Analysis Event which is applied for Real-Time Scoring.  This second video will walk you thru the configuration of Analysis Event for Real Time Scoring and validating that a predictions job has been executed based on new input data.    

Video Author:                     Christophe Morfin Original Post Date:            October 6, 2017 Applicable Releases:        ThingWorx Analytics 8.1   Description: This video covers the new features of ThingWorx Analytics Builder 8.1      

Video Author:                     Christophe Morfin Original Post Date:            October 2, 2017 Applicable Releases:        ThingWorx Analytics 8.1   Description:​ In this video we will walk thru a few steps to ensure the installation process was successful.    

Video Author:                     Christophe Morfin Original Post Date:            October 2, 2017 Applicable Releases:        ThingWorx Analytics 8.1   Description:​ In this video we will walk thru the installation steps of ThingWorx Analytics Server 8.1.  This covers the Native Linux installation though the steps will be similar for a docker installation on Windows or Linux.    

Video Author:                     Christophe Morfin Original Post Date:            September 26, 2017 Applicable Releases:        ThingWorx Analytics 8.0 & 8.1   Description:​ This video shows the commands to execute to deploy the training and results microservices as docker container.  This is based on Docker Toolbox to highlight the specific settings required on Toolbox.    

Video Author:                     Mohammed Amine Chehaibi Original Post Date:            June 29, 2017 Applicable Releases:        ThingWorx Analytics 8.0 to 8.1   Description:​ In this video you will learn how to: To bind a property of an existing entity to the KEPServerEx Data Feed To create an Alert on that property and monitor it's behavior