IoT Tips

This video will walk you through the first steps of how to set-up Analytics Manager for Real-Time Scoring. More specifically this video demonstrates how to create an Analysis Provider and start ThingPredictor Agent. NOTE: For version 8.1 the startup command for the Agent has changed view the command in PTC Help center.   Updated Link for access to this video:  ThingWorx Analytics Manager: Create an Analysis Provider & Start the ThingPredictor Agent                                  

In this part of the Troubleshooting blog series, we will review the process on how to restart individual services essential to the ThingWorx Analytics Application within the Virtual Machine Appliance.   Services have stopped, and I cannot run my Analytics jobs! In some cases, we have had users encounter issues where a system or process has halted and they are unable to proceed with their tasks. This can be due to a myriad of reasons, ranging from OS hanging issues to memory issues with certain components.   As we covered earlier in Part II, the ThingWorx Analytics Application is installed in a CentOS (Linux) Operating System. As with most Linux Operating Systems, you have the ability to manually check and restart processes as needed.   Steps to Restart Services   With how the Application is installed and configured, the services for functionality should auto start when you boot up the VM. You will have to verify that the Appliance is functional by running your desired API call.   If a system is not functioning as expected, you will receive an error in your output when you POST an API call. Some errors are very specific and you can search the Knowledge Database for any existing Knowledge Articles that may solve the issue.   For error messages that do not have an existing article, you may want to attempted the following   Method 1:   If you are encountering issues, and are unsure what process is not working correctly, we would recommend a full Application restart. This involves restarting the Virtual Machine Appliance via the command line terminal.   We would recommend that you use the following command, as root user or using SUDO, as this is known as a “Graceful restart” ​sudo reboot -h now   This will restart the virtual machine safely, and once you are back up and running you can run your API calls to verify functionality. This should resolve any incremental issues you may have faced.   Method 2:   If you want to restart an individual service, there is a particular start order that needs to be followed to make sure the Application is operating as expected.   The first step is check what services are not running, you can use the following command to check what is running and its current status: service –status-all   The services you are looking for are the following: Zookeeper PostgreSQL Server GridWorker(s) Tomcat   If a particular service is not on the running list, you will have to manually start them by using the service start command. service [name of service] start e.g. service tomcat start You may be prompted for the root password   You can verify that the services are operating by running the status check command as described above.   If you need to restart all services, we have a specific start order, this is important to do as there are some dependencies such as Postgres for the GridWorker(s) and Tomcat to use.   The start order is as follows: Zookeeper PostgreSQL Server GridWorker(s) Tomcat   After completing the restart, and verifying that the services are running, run your desired API call.

Concepts of Anomaly Detection used in ThingWatcher ThingWatcher is based on anomaly detection with the normal distribution. What does that mean? Actually,  normally distributed metrics follow a set of probabilistic rules. Upcoming values who follow those rules are recognized as being “normal” or “usual”. Whereas value who break those rules are recognized as being unusual. What is a normal distribution? A normal distribution is a very common probability distribution. In real life, the normal distribution approximates many natural phenomena. A data set is known as “normally distributed” when most of the data aggregate around it's mean, in a symmetric way. Also, it's extreme values get less and less likely to appear. Example When a factory is making 1 kg sugar bags it doesn’t always produce exactly 1 kg. In reality, it is around 1 kg. Most of the time very close to 1 kg and very rarely far from 1 kg. Indeed, the production of 1 kg sugar bag follows a normal distribution. Mathematical rules When a metric appears to be normally distributed it follows some interesting law. As does the sugar bag example. The mean and the median are the same. Both are equal to 1000. It’s because of  the perfectly symmetric “bell-shape” It is the standard deviation called sigma σ that defines how the normal distribution is spread around the mean. In this example σ = 20 68% of all values fall between [mean-σ; mean+σ] For the sugar bag [980; 1020] 95% of all values fall between [mean-2*σ; mean+2*σ] For the sugar bag [960; 1040] 99,7% of all values fall between [mean-3*σ; mean+3*σ] For the sugar bag [940; 1060] The last 3 rules are also known as the 68–95–99.7 rule also called the three-sigma rule of thumb When the rules get broken: it’s an anomaly As previously stated, When a system has been proven normally distributed, it follows a set of rules. Those rules become the model representing the normal behavior of the metric. Under normal conditions, upcoming values will match the normal distribution and the model will be followed. But what happens when the rules get broken? This is when things turn different as something unusual is happening. In theory, in a normal distribution, no values are impossible. If the weights of the bags of sugar were really distributed, we would probably find a bag of sugar of 860 g every billion products. In reality, we approximate this sugar bag example as normally distributed. Also, almost impossible value are approximated as impossible Techniques of Anomaly Detection Technique n°1: outlier value An almost impossible value could be considered as an anomaly. When the value deviates too much from the mean, let’s say by ± 4σ, then we can consider this almost impossible value as an anomaly. (This limit can also be calculated using the percentile). Sugar bags who weigh less than 920 g or more than 1080 g are considered anomalous. Chances are, there is a problem in the production chain. This provides a simple way to define maximum and minimum thresholds. Technique 2: detecting change in the normal distribution Technique n°2 can detect unusual distribution fast, using only some points. But it can’t detect anomalies who move from one sigma σ to another in a usual manner. To detect this kind of anomaly we use a “window” of n last elements. If the mean and standard derivation of this window change too much from usual then we can deduce an anomaly. Using a big window with a lot of values is more stable, but it requires more time to detect the anomaly. The bigger the window is the more stable it becomes. But it would require more time to detect the anomaly as it needs to aggregate more values for the detection.

In this Blog, we will share some light about Gradient boost, which is a default algorithm in our Analytics platform. We will touch on: 1) The main purpose of Gradient boost and how the technique works. 2) We will look at advantages and constraint. 3) Last some “nice to know” tips when working with Gradient. Gradient boost is a machine learning technique which main purpose is to help weak prediction models become stronger. Gradient boost works by building one tree at a time, and correct errors made by previously tree. The theory support reweights of edges which allows badly weight edges to get reweighted. For example the misclassified gain weight and those weights which are classified correctly, lose weight. It is kind of the same strategy when dealing with stocks; you balance the investment between bonds and share. An analog could also be done to illnesses; If a doctor informs that you have a rare disease, you want to make sure to get a few more opinions from other doctors, You will evaluate all the information to make a more correct decision about how to cure yourself. Why use gradient boost: - Gradient boost provides the user with a powerful tool to boost/improve weak prediction models. - Gradient boost works well with regression and classification problems, therefore Decision tree can benefit from applying gradient boost. - Gradient bo​ost is known in the industry, to be one of the best techniques to use when dealing with model improvement. - Gradient boost uses stagewise fashion, in this way each time it adjust a tree, it does not go back and readjust when dealing with the next tree. As with all machine learning algorithms gradient boost also have some constraint: - There is a change of overfitting. “Nice to know” tips: - A natural way to reduce this risk of overfitting would be to monitor and adjust the iterations. - The depth of the tree might have an influence on the prediction error, observe what happens if the depth is a stump/1 level deep.

This Blog presents a simple Java utility to validate the deployment of ThingWatcher. It is important to note that the utility used is not a real life situation, the intent was to keep it as simple as possible in order to achieve its aim: validation of the deployment. An understanding of Java IDE (such as Eclipse) is necessary in order to run the utility with relevant dependency and classpath setup. Those are beyond the scope of this posting. We will cover the following points: Pre-Requisites Using the sample utility Code walk through Validate training job creation Validate model job creation Update for ThingWorx Analytics 8.0 Pre-requisites A strict adherence to the ThingWatcher deployment guide is recommended in order to first deploy training and model microservices as well as to familiarize yourself with ThingWatcher APIs. Prior to testing ThingWatcher, both the training and model microservices should be up and running The media for ThingWatcher (including model and training micro-service) should be downloaded from PTC Software Download page . The commands to deploy the micro-services will vary depending on the platform used and are presented in the ThingWatcher deployment guide. As a reference example, on Windows the command will be similar to the following: Start Docker: Start > Program > Docker > Docker Quick Start Terminal Load model micro service tar $ docker load < "D:\PTC\MED-61147-CD-522_F000_ThingWorx-Analytics-ThingWatcher-52-2\components\ModelService\ModelService\model-service.tar"     3. Install model service: $ docker run -d -p 8080:8080 -v '/d/TWatcherStorage/model:/data/models' -v '/d/TWatcherStorage/db:/tmp/' twxml/model-service:1.0 -Dfile.storage.path=/data/models -jar maven/model-1.0.jar server maven/standalone-evaluator.yml     4. Load training micro service tar file                         $ docker load < "D:\PTC\MED-61147-CD-522_F000_ThingWorx-Analytics-ThingWatcher-52-2\components\TrainingService\TrainingService\training-service.tar"     5. Install training service                         $ docker run -d -p 8090:8080  twxml/training-service:1.0.0  -Dmodel.destination.uri=model://192.168.99.100:8080/models -jar maven/training-standalone-1.0.0-bin.jar server /maven/training-standalone-single.yml Note: the -Dmodel.destination.uri points here to the model micro-service host. To find the ip address, enter docker-machine ip on the model micro-service docker machine.     6. Validate micro-services deployment: Execute docker ps  and confirmed that both services are up, as in the following example: CONTAINER ID        IMAGE                          COMMAND                      CREATED            STATUS              PORTS NAMES 5b6a29b95611        twxml/training-service:1.0.0  "java -Dmodel.destina"  13 days ago        Up 44 minutes      8081/tcp, 0.0.0.0:8090->8080/tcp  modest_albattani 8c13c0bc910e        twxml/model-service:1.0        "java -Dfile.storage."      2 weeks ago        Up 44 minutes      0.0.0.0:8080->8080/tcp, 8081/tcp  thirsty_ptolemy   Using the sample utility Download the attachment Main.java Import Main.java into Eclipse (or IDE of choice) with the ThingWatcher dependencies added in classpath. Update the trainingBaseURI (see below) to points to the training micro-services. The utility should be ready to execute. Code walk through The code declares a thingwatcher in the following snippet: ThingWatcher thingwatcher = new ThingWatcherBuilder() .certainty(90.0) .trainingDataDuration(60) .trainingDataDurationUnit(DurationUnit.SECOND) .trainingBaseURI("http://192.168.99.100:8090/training") .getThingWatcher(); In the above code it is important to update the trainingBaseURI argument with the correct ip address and port for the training micro-service host. The code then loops 10000 times and sends a new value, which simulates the sensor data, at a simulated 100 ms interval. The value is computed as Math.sin(i) for the whole calibrating phase and most of the monitoring phase too. We artificially introduce an anomaly by sending a value of Math.incremetExact(i) between the 9000 th and 9900 th iterations. During the Monitoring phase, the code logs the value, the anomalous status and the thingwatcher state. It is advised to save the output to a file in order to review the logging once the utility has run. In Eclipse this can be done by selecting the Main.java with right mouse button > Run As… > Run Configuration > Common and tick Output File under the Standard Input and Output, and specify a location for the output file. A review of the output log file will shows that somewhere between timestamp 900000 and 990000, the isAnomalousValue is true. Note that this does not starts and ends exactly at 900000 and 990000, as ThingWatcher needs a few occurrences before reporting it as anomaly. Sample output indicating an anomalous state: [main] INFO com.thingworx.analytics.demo.Main - Value = 901700,9017.0,-9016.403802019577 [main] INFO com.thingworx.analytics.demo.Main - isAnomalousValue = true [main] INFO com.thingworx.analytics.demo.Main - ThingWatcherStat = MONITORING As part of validating the successful deployment of ThingWatcher, it is recommended to validate the correct creation of a training and model job. Validate training job creation In order to validate the successful creation of a training job, execute a GET request to the training micro service : http://192.168.99.100:8090/training (update the ip address to the one on your system) This should return a COMPLETED job whose body starts with something similar to: Validate model job creation In order to validate the successful creation of a model job, execute a GET request to http://192.168.99.100:8080/models (update the ip address to the one on your system) to see all the models that have been created. For example: Alternatively, click (or use) the URI reported in the training job output, here http://192.168.99.100:8080/models/6/pmml.xml, to see the complete model definition. The output will be similar to: When this sample test runs correctly, the ThingWatcher deployment has been validated. Update for ThingWorx Analytics 8.0 Deploying the microservices, see Video Link : 1937 Updated Java code: see Does anyone know how to use java api to achieve anomaly detection with Thingwatcher8.0? To Note: The utility provided is for testing purpose only. The code does not represent any kind of best practice and is not meant to be a perfect java coding example. It is provided as is with no guarantee.

This blog is about Decision tree and it is aimed at providing the Analytics user with additional information about our default algorithm; Decision tree. More specifically we will clarify what structures builds the Decision tree, understand the purpose of these structures, and last we will look at a few examples of pros and cons of applying Decision tree. Decision tree is a great tool to help us making good decisions based on a huge amount of data. The algorithm maps information provided from the dataset and constructs a tree to predict our goal. ​ Classification and regression trees are the structures behind Decision tree  – Therefore when we refer to Decision tree we collectively include classification and regression as being part of Decision tree. But what is the difference between Classification and regression? 1) Classification can be used for predicting dependent categorical variables. For example if needed to predict what type of failure occurs with a machine, or what type of car a person would buy it would be a classification tree. 2) Regression is used for dependent continues numerical variables. For example if you want to predict the amount of sugar in a person’s blood or you need to predict the price of oil per gallon in 2020, regression is uses for the prediction. Regression is addressing predictions, where the value can be continues valued, and classification tree predict the correct label/type for the class. Example of a classification tree: Keep in mind that it is the goal variable that determines the type of decision tree needed. Using Decision tree is a powerful tool for prediction: Easy to understand and interpret. Help us to make the best decisions on the basis of existing information. Can handle missing values without needing to resort. Considerations: As with all analytics models, there are also limitations of the decision tree. Users must be aware of, Decision trees can be subject to overfitting and underfitting, particularly when using a small dataset. High correlation between different variables may cause very high model accuracy.

Steps Get the IP address of the ThingWorx Analytics Server Type ip a Put that IP address into the desired web browser Your IP address may be different from the one in the picture above Add the port number of the server to the end of the IP address The Default  port number is 8080 Make sure to put a colon " : " between the end of the IP address and the start of the port number The port number could be different in some cases, depending if it was configured differently during installation Hit Enter and the main page will load.

Scoring is the process of making the prediction on the basis of the available data. Scoring is the process of assigning a predicted outcome to an individual record based on running that record’s conditions through the trained model. It allows you to request and retrieve individual record level prediction scores for a defined data set for a set of prediction topics. The accuracy of the score will likely be a direct reflection of the error rate produced by the Trained Model. Why the score value exceeds min or max value range of feature There are a few concepts to address with regards to this: Scoring outputs: It is important to note that when training an analytics model, the method is to create a generalizable model from a relatively small training dataset. By its nature, we expect the training process to see a limited subset and not an exhaustive list of all possible values for many constraints, especially time and practicality. As such, these generalized models will be expected to handle unseen data in the form of new combinations or values outside of previously observed ranges (more on this below). One common way to see scores that exceed the observed ranges in training, under the assumption that the goals are continuous, is to use prescriptive scoring. Prescriptive scoring attempts to find optimal values for lever, meaning tunable, features in order to maximize or minimize score values. Min/Max constraints: these are constraints that are placed upon the inputs for training and expected inputs for scoring. For training: If theses ranges were provided as part of the upload process, then training will raise exceptions regarding invalid data. However, if the ranges are not provided - they will be inferred from the data and, as such, training will not see values outside of observed ranges. For scoring: validation of the ranges will only be performed on the inputs - not the outputs. It is very important to note that the handling of these "constraints" is dependent upon the data type. For categorical (e.g. colors) and ordinal data (e.g. shirt sizes), the constraints are strict and data that was not observed in training will raise exceptions during scoring. However, for continuous values (e.g. temperature ranges) these constraints are more informational in nature. For predictive scoring, our code will accept records with values outside of those ranges. The rule of thumb is that values slightly outside these ranges are acceptable and that as the values stray farther from the ranges, the accuracy of the model degrades very quickly. For prescriptive scoring, these constraints are used to determine the acceptable ranges of values to try when determining the optimal values. Values outside of these constraints will NOT be tried. How to handle goal values while scoring What should be the value for the goal(objective TRUE) column in new data which would be scored using existing prediction model? <Dataset for making prediction model> Independent value goal field -0.65 0 -0.75 0 -0.85 0 0.85 1 0.45 1 ~~~ ~~~ <New data to be scored> Independent value goal field -0.25 ?? 0.35 ?? -0.45 ?? 0.95 ?? 0.15 ?? ~~~ ~~~ Now scoring, by its definition, does not take into consideration the goal column when being run. Seeing as the goal column above is a Boolean, we can populate the yet to be scored records with either a 0 or 1 and it won’t matter when it comes to scoring.

ThingWorx Analytics Interactive API Guide is a great way for users to familiarize themselves with ThingWorx Analytics APIs calls.  It even gives users the ability to run jobs through its interface.  This blog post will cover how to access the ThingWorx Analytics Interactive API Guide installed on a Virtual Machine or Standalone Server. Steps Get the IP address of the ThingWorx Analytics Server Type ​ip a ​Put that IP address into the desired web browser ​Your IP address may be different from the one in the picture above Add the port number of the server to the end of the IP address ​The Default  port number is 8080 Make sure to put a colon " : " between the end of the IP address and the start of the port number The port number could be different in some cases, depending if it was configured differently during installation ​Hit Enter and the main page will load.

Metrics for Model evaluation used in ThingWorx Analytics In ThingWorx Analytics, we consider different kinds of metrics to evaluate our models. The choice of metric completely depends on the type of model and the implementation plan of the model. After you are finished building your model, these 3 metrics will help you in evaluating your model accuracy. Here are below further explanations about the 3 metrics used. 1-The ROC Curve: To understand what is ROC (Receiver operating characteristic) curve, let's look at the confusion matrix below. We observe that for a probabilistic model, we get a different value for each metric. Hence, for each sensitivity, we get a different specificity. The two vary as follows: The ROC curve is the plot between sensitivity and (1- specificity). (1- specificity) is also known as false positive rate and sensitivity is also known as True Positive rate. Following is the ROC curve for the case in hand Let’s take an example of threshold = 0.5 (refer to confusion matrix). Here is the confusion matrix: As you can see, the sensitivity at this threshold is 99.6% and the (1-specificity) is ~60%. This coordinate becomes on point in our ROC curve. To bring this curve down to a single number, we find the area under this curve (AUC). Note that the area of the entire square is 1*1 = 1. Hence AUC itself is the ratio under the curve and the total area. For the case in hand, we get AUC ROC as 96.4%. Following are a few thumb rules: .90-1 = excellent (A) .80-.90 = good (B) .70-.80 = fair (C) .60-.70 = poor (D) .50-.60 = fail (F) We see that we fall under the excellent band for the current model. But this might simply be over-fitting. In such cases, it becomes very important to have in-time and out-of-time validations. Points to Remember: For a model which gives a class as an output, it will be represented as a single point in ROC plot. Such models cannot be compared with each other as the judgment needs to be taken on a single metric and not using multiple metrics. For instance, a model with parameters (0.2,0.8) and model with parameter (0.8,0.2) can be coming out of the same model, hence these metrics should not be directly compared. 2-Root Mean Squared Error (RMSE) RMSE is the most popular evaluation metric used in regression problems. It follows an assumption that error are unbiased and follow a normal distribution. Here are the key points to consider on RMSE: The power of ‘square root’ empowers this metric to show large number deviations. The ‘squared’ nature of this metric helps to deliver more robust results which prevent canceling the positive and negative error values. In other words, this metric aptly displays the plausible magnitude of the error term. It avoids the use of absolute error values which is highly undesirable in mathematical calculations. When we have more samples, reconstructing the error distribution using RMSE is considered to be more reliable. RMSE is highly affected by outlier values. Hence, make sure you’ve removed outliers from your data set prior to using this metric. As compared to mean absolute error, RMSE gives higher weighting and punishes large errors. 3-Pearson Correlation Coefficient This metric measures how highly correlated are two variables and is measured from -1 to +1. A Pearson Correlation Coefficient of 1 indicates that the data objects are perfectly correlated but in this case, a score of -1 means that the data objects are not correlated. In other words, the Pearson Correlation score quantifies how well two data objects fit a line. There are several benefits to using this type of metric. The first is that the accuracy of the score increases when data is not normalized. As a result, this metric can be used when quantities (i.e. scores) varies. Another benefit is that the Pearson Correlation score can correct for any scaling within an attribute, while the final score is still being tabulated. Thus, objects that describe the same data but use different values can still be used. The below figure demonstrates how the Pearson Correlation score may appear if graphed. The chart demonstrates the Pearson Correlation Coefficient. The axes are the scores given by the labeled critics and the similarity of the scores given by both critics in regards to certain an_items. In essence, the Pearson Correlation score finds the ratio between the covariance and the standard deviation of both objects. In the mathematical form, the score can be described as: In this equation, (x,y) refers to the data objects and N is the total number of attributes

This is part of the continuing series of Blog posts regarding Troubleshooting the Application, this article will discuss more advance issues that some clients and customer have encountered while building or using ThingWorx Analytics. Packer Script Error – Unable to Download CentOS Image As the application is developed and built inside a CentOS image, the ThingWorx Analytics Packer Script tool for Virtual Machine Appliance creation utilizes the CentOS mirror repository in the creation process. When the end user is attempting to build the Virtual Machine Appliance with the Packer Script media creation tool, part of the process is to download the CentOS 7 ISO image file as the basis for the operating system that the ThingWorx Analytics Server software will be installed to. If CentOS updates or changes their mirror links for the source file ISO, you may encounter the following error: ==> virtualbox-iso: Downloading or copying Guest additions virtualbox-iso: Downloading or copying: file:///C:/Program%20Files/Oracle/VirtualBox/VBoxGuestAdditions.iso ==> virtualbox-iso: Downloading or copying ISO virtualbox-iso: Downloading or copying: file:///local-file-repo/CentOS-7-x86_64-Minimal-1511.iso virtualbox-iso: Error downloading: open local-file-repo/CentOS-7-x86_64-Minimal-1511.iso: The system cannot find the path specified. virtualbox-iso: Downloading or copying: http://mirror.spro.net/centos/7/isos/x86_64/CentOS-7-x86_64-Minimal-1511.iso virtualbox-iso: Error downloading: checksums didn't match expected: 88c0437f0a14c6e2c94426df9d43cd67 ==> virtualbox-iso: ISO download failed. Build 'virtualbox-iso' errored: ISO download failed. ==> Some builds didn't complete successfully and had errors: --> virtualbox-iso: ISO download failed. ==> Builds finished but no artifacts were created. Solution Method 1: Configuration File Replacement We have created a custom JSON configuration file that resolves the mirror issue for CentOS 7 v1611. You can download the JSON file here; you may have to right-click and “save link as” a JSON extension file. Also note, you will have to save/rename this JSON file as neuron-solo-variables.json. Using this file, navigate to your Packer Script builder directory, usually this is found in the following path: <PATH>\ThingWorx-Analytics-Server-Standalone\components\vm-builder\neuron-vm-builder Copy the new JSON file into this directory, and replace the current existing copy. You can now re-run the Packer Script for your desired Virtual Machine Appliance output. Method 2: Manual Configuration File Adjustment You will have to locate an active mirror for CentOS 7. A list of current active mirrors can be found here. When selecting a mirror, you will need to select the Minimal ISO install, as this is the base image that is used for the VM creation. Next, you will have to open the current neuron-solo-variables.json configuration file located in the <PATH>\ThingWorx-Analytics-Server-Standalone\components\vm-builder\neuron-vm-builder directory. You will have to replace the os_image_download_url value with an active Mirror URL from the list above. Next, for the os_iso_md5_checksum variable, you will need to replace the entry with the new SHA256 checksum from CentOS, which can be located here. Default Settings: New Settings: Save changes and close the neuron-solo-variables.json configuration file. CentOS has switched over from MD5 to SHA256 checksums. Even though in the following the variable name has “MD5” in the string, we will be modifying a second JSON configuration file to address this. In the same directory that we are currently working in, open the neuron-solo.json configuration file. You will need to modify the attribute iso_checksum_type to sha256 Default Settings: New Settings: Save changes and close the neuron-solo.json configuration file. You can now re-run the Packer Script for your desired Virtual Machine Appliance output.

ThingWorx Analytics Builder - Upload Data   This video walks you through how to upload data and shows the configuration settings. Please be aware that shown configuration settings page is different for version 8.1.   Updated Link for access to this video:  ThingWorx Analytics Builder: Upload Data

This is the Second Part of Getting Started wth ThingWorx Analytics. In this video,we would be using Postman.   During this video you will learn:   -Creating a Dataset -Entering the Dataset configuration -Uploading the CSV data File to TWA Server   Updated Link for access to this video:  Getting Started with ThingWorx Analytics Part-2

Signals indicate the predictive strength or weakness of specific features on the goal variable. Use Signals to explore which features are important to predicting outcomes, and which are not. Note: Please be aware that the video states that a model has to be created before Signals can run, but this is no longer the case for version 8.1.   Updated Link for access to this video:  Create Signals In ThingWorx Analytics Builder

In this video you would see how to start to use your already created Virtual Image of ThingWorx Analytics using Oracle Virtual Box. This Video is Part-1 of the Series Getting Started with ThingWorx Analytics.   Updated Link for access to this video:  Getting Started with ThingWorx Analytics: Part 1 of 2

In this video we cover the process of installing ThingWorx Analytics Server 52.1. Make sure to have reviewed the part 1 video about pre requisite   Updated Link for access to this video:  Installing ThingWorx Analytics Server: Part 2 of 2

In this video we review the prequisite needed prior of installing ThingWorx analytics server 52.1   Updated Link for access to this video:  Installing ThingWorx Analytics Server: Part 1 - Prerequisites

ThingWorx Analytics is capable of being assembled in multiple Operating Systems. In this post, we will discuss common issues that have been encountered by other users. Permissions Denied – Read/Write access to Third Party Components This is encountered when executing the desired Shell script to begin the creation process. In MacOS and Linux you may encounter a “Permissions Denied” error on the two required components in the creation, the packer-post-processor-vhd and packer components. Error Message This will result in a Terminal dialog message that will read “Process Completed, No Artifacts Created”. This indicates that the Packer Script has failed to complete the task, and the desired appliance images were not created. To correct this issue, you will have to change the permissions of the packer-post-processor-vhd and packer components to be able to be read and executable by the user account that is attempting to create the appliance. Solution Run the following commands in the Virtual Machine terminal (you may need to run as SUDO or as Root): chmod +x packer-post-processor-vhd ​chmod +x packer After running the above command, run the Shell script of the desired VM Appliance output. This should resolve the issue with “Permission Denied” while executing the build scripts. Error Starting Appliance in VirtualBox Users have experienced this issue at the first run of the Appliance, right after it has been assembled. This issue is unique to VirtualBox versions 5.0 and above. Error Message – Dialog Box If you encounter the error depicted below, please check under settings for the imported OVA for any errors: This issue is the result of invalid settings in the Appliance Configuration. You will need to check for Invalid Settings, by navigating to the Settings Menu for the Appliance: The “Invalid settings detected” indicates that when the Product was assembled, some configuration settings were not applied correctly by the creation tool scripts. Solution Hover your mouse over the settings and it will direct you to cause, in this case it is due to remote monitor setup. Just change the settings in Display (Remote Display Tab) by unchecking the Enable Server button. Press OK after unchecking the “Enable Server” option, and start the Appliance.

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 ?

In this video we cover the installation of the UploadThing module. This video applies to ThingWorx Analytics 52.2 till 8.0. This is no longer applicable with ThingWorx Analytics 8.1   Useful links: How to copy files from Windows to Linux Updated Link for access to this video:  Installing Thingworx Analytics Builder: Part 3 of 3  

In this video we cover the different configuration steps required for ThingWorx Analytics Builder extension This video applies to ThingWorx Analytics 52.1 till 8.1.   Note though: - this video uses Classic Composer, the same operations can be done using the New Composer starting with version 8.0 as illustrated in the Help Center - For release 8.1, the Settings menu differs from previous versions, see Video Link : 2079 between times 00:12 sec to 00:40 sec for up to date menu selection.   Updated Link for access to this video:  Installing Thingworx Analytics Builder: Part 2 of 3