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  Whether you’re new to ThingWorx or you’re a seasoned user, understanding the Thing Model is key to accelerating your IoT development. Today, I’ll dive into what ThingShapes, ThingTemplates and Things are and how to use them to accelerate development.   Before I dive into the definitions of these concepts, let’s first consider the wide array of machines that exist out there in world. The variety is huge—there’s MRI machines, 3D printers, laser cutters, CNC machines, tractors, and so much more.   At their core, all MRI machines share similar properties and capabilities—they have a name, a physical location, a magnetic strength, a radio frequency current, and the ability to visually display what’s going on inside the human body. There are, however, different types of MRI machines, and, while they are fundamentally the same type of machine, there are notable differences as well. When creating our IoT app, it’s important that we have a way to model these differences so that we can cascade changes across entities and reduce development time.   Let’s walk through an example using MRI machines. Consider the various MRI machines that exist today; there’s the traditional closed MRI machine, the open MRI machine and the standing/sitting MRI machine.   To represent the fundamental properties (i.e., characteristics or readings) and services (i.e., functionality) of a generic MRI machine—name, location, magnetic strength, etc.—we’ll create a ThingTemplate. The ThingTemplate is the general definition/representation of the real-world physical thing (i.e. the MRI machine) that is being modeled. You can think of a ThingTemplate as a blueprint of what you’re modeling. A ThingTemplate defines what a Thing is; if you’re familiar with object-oriented programming, a ThingTemplate is similar to the concept of inheritance; it defines a “is a” relationship. Using our ThingTemplate, we’re able to create multiple instances of the template that inherit the properties and services from that template. If you have 100 MRI machines in a particular region, rather than updating each one separately, simply updating the template will allow you to propagate these changes.   Let’s say that, of our 100 MRI machines, 40 are traditional closed machines, 30 are open machines and 30 are standing/sitting machines. The traditional machines have a specific diameter of the opening where the patient goes in to lay down and the sitting/standing machine may have a particular height of the seat where the patient sits. Due to the nature of the machines having unique components/parts, the different types of machines have difference maintenance service.   To model each of these “add-on” properties, we’ll want to create a ThingShape. A ThingShape is a representation of particular properties or services that may optionally come in some versions of the machine but not others. The ThingShape is a single feature or piece of the physical thing that’s being modeled. You can think of a ThingShape as a reusable part, or a set of properties/services that comes with some versions, but not all. A ThingShape defines what a Thing has; if you’re familiar with object-oriented programming, a ThingShape is similar to the concept of composition; it defines a “has a” relationship. So, for our MRI example, we could create one ThingShape for the standing MRI and a second ThingShape for the closed MRI. The StandingMRIThingShape would have a property of “SeatHeight” and a service of “StandingMRIMaintenanceService.” The ClosedMRIThingShape would have a property of “opening diameter” and a service of “ClosedMRIMaintenanceService.” Just like a ThingTemplate, the properties and services that make up a ThingShape are also inherited by the instances that use that ThingShape.   Finally, Things. A Thing is simply an instance of a ThingTemplate with (optionally) ThingShapes added for additional unique properties/services.   Let’s say we want to model a single closed MRI machine. We’ll represent the machine as a Thing that inherits from Templates and Shapes. We’ll start with the MRIMachineThingTemplate so that we can create an MRI Machine Thing (i.e., instance).   Since this is a closed MRI machine and has the additional property of opening diameter, we’ll want to make sure we include that property. To do this, we’ll add the ClosedMRIThingShape.   Viola! We now have a digital twin of our closed MRI machine with all the base properties of an MRI machines from our MRIMachineThingTemplate and all the special add-ons of the closed version with our ClosedMRIMachineThingShape.   Here’s a visual recap of what we just modeled.   If you’re looking for even further guidance on how to model your data with the Thing Model, check out the Data Model Introduction guide on the Developer Portal to get started and the Design Your Data Model guide to learn even more.   Happy data modeling!   Stay connected, Kaya  
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In this post I show how to use Federation in ThingWorx to execute services on a different ThingWorx platform instance. In the use case below I set up one ThingWorx instance in the Factory and another instance in the Cloud, whereby the latter is executing a service which is actually running on the former.   Please find the document in attachment.   HTH, Alessio Marchetti  
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Remote Monitoring of Assets Benchmark   As @ttielebein introduced previously, one of the missions of the IOT Enterprise Deployment Center (EDC) is to publish benchmarks that showcase the ThingWorx Platform deployed to solve real-world IOT business problems.    Our goal is that these benchmarks can be used as a reference or baseline for architects working on their own implementations... showing not only a successful at-scale implementation, but also what happens when that same implementation is pushed to ...or even past... it's limits.   Please find the first installment attached - a reference benchmark demonstrating ThingWorx deployed to monitor 15,000 assets with a high-volume of data properties per asset.  Over 250 hours of simulations were conducted as part of producing this benchmark.   The IOT EDC team will be monitoring this post (as well as our other posts in the IOT Tech Tips forum) to answer any questions we can about the approaches taken in designing, deploying and simulating this implementation.    As the team will publish more benchmarks like this will be published in the future, we also greatly value any feedback you have that can help us to improve the content for future documents.
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  Hello, everyone!   With the release of ThingWorx 8.5, we’ve incorporated a lot of new functionality into our manufacturing and service apps. To cover a few, I’ve included the list below. We created a manufacturing common layer extension to bundle all the PTC-offered IoT apps (Operator Advisor, Asset Advisor, Production KPIs and Controls Advisor) into one extension to enable you to use them even more quickly. We added a UI to Operator Advisor to strengthen your development of work instructions. We introduced new shift and crew data models and user interfaces to standardize how to track workers’ shifts and crew availability. We also introduced Flexible KPIs to help you more rapidly develop apps to calculate common metrics. We enhanced the Operator Advisor MPMLink connector to allow users to access navigation criteria for filtering parameters based on required criteria with support for standard processes. We incorporated multiple context supports for assets—like different business units, separating maintenance views from production views or segmenting sites by location—to allow segmentation based on role and responsibility, showing the power of ThingWorx networks and permissions.   Today, I’d like to highlight one of these areas in particular: flexible KPI calculations.   I spoke with one of product managers, Ward (who you may recognize from this post) to learn more about what this new feature does and the value it brings the business. Here’s what he said:   Kaya: Why did we create flexible KPIs? What was the challenge users were facing that led us to create them? Ward: While there is an industry standard for KPIs such as OEE, availability, productivity and quality, many customers choose to customize the calculations slightly and use their own specific versions in their decision-making process or production monitoring.    Kaya: What do the flexible KPIs do? Can you provide an example? Ward: KPIs can now be customized by ThingTemplate; this allows users to calculate KPIs differently for different “classes” of things. Imagine you have a group of CNC machines and a group of pumps. You want to calculate the availability of each group, but the availability calculation for the CNC machines may differ slightly from the same availability calculations for pumps. With our new flexible KPIs, you’re able to customize the availability calculation to make slight tweaks or changes based on differences in machines or devices. So, you can calculate the availability for both your CNC machines and for your pumps using your customized availability calculations. You can also create your own KPIs to calculate metrics like safety incidents or waste.   Kaya: Let’s dive a little deeper there. If I want to create a quality station for my robot with custom KPIs, how exactly would I do that? Ward: Let’s consider OEE. We have an OEE ThingShape applied to our ISA95 physical ThingTemplate. This shape has services to perform the OEE calculation. You’re able to customize the service on this template, so you have the flexibility to change the way you perform OEE calculations. Now, let’s say you want to add a new KPI like mean time between repair (MTBR). To do so, you would create a MTBR ThingShape and add it to the ThingTemplates where you want OEE calculated. Then, you would update the KPI manager service GetKPINames to add your ThingShape to the list of KPIs to be executed each iteration. The SCO apps will then execute your MTBR service along with the other KPIs.   Kaya: What are their use cases? How does they improve the business? Ward: Customers can have their cells roll up OEE based on the worse performing asset and have their operations roll up on a different criterion.  If the customer wants to use a modified OEE on a machine that includes the size of the crew operating it, they can now do so on that one machine, on classes of machines or on all machines.   Kaya: Wow. You were certainly busy with 8.5 with all these new features. Can you tell me what you’re most excited about for 9.0? Ward: I’m most looking forward to High Availability. The ability to have multiple servers in an active-active mode allows me to do more processing in ThingWorx and provide a level of reliability to my customers. (Look out for info on this exciting new functionality in the future!)   Ready to get started using the flexible KPIs yourself? Check out the ThingWorx Apps Customization Guide! While you’re at it, be sure to also check out the other new features in 8.5 listed above!   Reach out with any questions and stay connected! Kaya
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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 
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The Axeda Platform has long had the ability to write custom logic to retrieve, manipulate and create data.  In the current versions of the Platform, there are two classes of API, Version 1 (v1) and Version 2 (v2).  The v1 APIs allow a developer to work with data on the Platform, but all of the APIs are subject to the maxQueryResults configuration property, which by default limits the number of results per query to 1000. For some subsets of data, this can be inadequate to process data.  In comes the v2 API, which introduces pagination. One of the first things a new user does when exploring the V2 API, is something like the following: HistoricalDataItemValueCriteria criteria = new HistoricalDataItemValueCriteria() criteria.assetId = '9701' criteria.startDate = '2014-07-23T12:33:00Z' criteria.endDate = '2014-07-23T12:44:00Z' DataItemBridge dbridge = com.axeda.sdk.v2.dsl.Bridges.dataItemBridge FindDataItemValueResult results = dbridge.findHistoricalValues(criteria)           And they get frustrated when they only get the same 100 rows of data.  Repeat after me: V2 API invocations (find operations) are limited to batches of 100 results at a time! But that's not the end of the story.  With a small change, the query above can be tuned to iterate through all results that match the search criteria:  HistoricalDataItemValueCriteria criteria = new HistoricalDataItemValueCriteria() criteria.assetId = '9701' criteria.startDate = '2014-07-23T12:33:00Z' criteria.endDate = '2014-07-23T12:44:00Z' criteria.pageNumber = 1 criteria.pageSize = 100 // Default. DataItemBridge dbridge = com.axeda.sdk.v2.dsl.Bridges.dataItemBridge FindDataItemValueResult results = dbridge.findHistoricalValues(criteria) tcount = 0 while ( (results = dbridge.findHistoricalValues(criteria)) != null  && tcount < results .totalCount) {   results.dataItems.each { res ->     tcount++   }   criteria.pageNumber = criteria.pageNumber + 1 }    I currently recommend that people avoid using the count() or countDomainObjectByCriteria() functions if you're then going to call a find.  Currently both the count*() and find functions compute total results, and doubles execution time of just those two calls.  Total count is only computed when running the first find() operation, so the code pattern above is so far the most efficient way I've seen to run these operations on the platform. So having covered how to do this in code (custom objects), let's turn our attention to the REST APIs - the other entry-point for using these capabilities.  The REST API doesn't offer a count*() function, but the first find() invocation (if using XML) brings back totalCount as part of the result set.  You can use this in your application to decide how many times to call the REST end-point to retrieve your data.  So for the example above: POST:  https://customer-sandbox.axeda.com/services/v2/dataItem/findHistoricalValues HEADERS: Content-Type: application/xml Accept: application/xml BODY: <?xml version="1.0" encoding="UTF-8"?> <HistoricalDataItemValueCriteria xmlns="http://www.axeda.com/services/v2" pageSize="100" pageNumber="1"> <assetId>9701</assetId> <StartDate>2014-07-23T12:33:00Z</StartDate> <endDate>2014-07-23T12:35:02Z</endDate> </HistoricalDataItemValueCriteria>      RESULTS: <v2:FindAssetResult totalCount="1882" xmlns:v2="http://www.axeda.com/services/v2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">    <v2:criteria pageSize="100" pageNumber="1">       <v2:name>*</v2:name>       <v2:propertyNames/>    </v2:criteria>    <v2:assets>    </v2:assets> </v2:FindAssetResult>      Or JSON: POST:  https://customer-sandbox.axeda.com/services/v2/dataItem/findHistoricalValues HEADERS: Content-Type: application/xml Accept: application/xml BODY: {   "id":  9701,   "startDate": "2014-07-23T12:33:00Z",   "endDate": "2014-07-23T12:35:02Z",   "pageNumber": 1,   "pageSize": 2 }      And that's how you work around the maxQueryResults limitation of the v1 APIs.  Some APIs do not currently have matching v2 Bridges (e.g. MobileLocation and DataItemAssociation), in which case the limitation will still apply.  Creative use of the query Criteria will allow you to work around these limitations as we continue to improve the V2 API. Regards, -Chris
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 Image Source: https://www.thefire.org/resources/spotlight/     Designed a super cool mashup? Have an innovative IoT app? Are you really proud of how you solved an IoT challenge? Are you using ThingWorx for a unique use case?   If so, we want to hear from you! I’m looking for a few ThingWorx developers that are interested in sharing their work to be showcased on the Ask Kaya blog! (Don’t worry—we can hide your confidential info and only share what you allow us to.) We’d love to highlight what our developers are doing with ThingWorx out in the real world. If you’re interested, comment below or message me directly!   As always, stay connected.  
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One of the signature features of the Axeda Platform is our alarm notification, signalling and auditing capabilities.   Our dashboard offers a simplified view into assets that are in an alarm state, and provides interaction between devices and operators.  For some customers the dashboard may be too extensive for their application needs.  The Axeda Platform from versions 6.6 onward provide a number of ways of interacting with Alarms to allow you to present this data to remote clients (Android, iOS, etc.) or to build extended business logic around alarm processing. If one were to create a remote management application for Android, for example, there are the REST APIs available to interact with Assets and Alarms.  For aggregate operations where network traffic and round-trip time can be a concern, we have our Scripto API also available that allows you to use the Custom Object functionality to deliver information on many different aggregating criteria, and allow developers to get the data needed to build the applications to solve their business requirements. Shown below is a REST API call you might make to retrieve all alarms between a certain time and date. POST:   https://INSTANCENAME/services/v2/rest/alarm/find <v2:AlarmCriteria xmlns:v2="http://www.axeda.com/services/v2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">    <v2:date xsi:type="v2:BetweenDateQuery">     <v2:start>2015-01-01T00:00:00.000Z</v2:start>     <v2:end>2015-01-31T23:59:59.000Z</v2:end>   </v2:date>   <v2:states/> </v2:AlarmCriteria>   In a custom object, this would like like the following: import static com.axeda.sdk.v2.dsl.Bridges.* import com.axeda.services.v2.* import com.axeda.sdk.v2.exception.* def q = new com.axeda.services.v2.BetweenDateQuery() q.start = new Date() q.end = new Date() ac = new AlarmCriteria(date:q) aresults = alarmBridge.find(ac)   Using the same API endpoint, here's how you would retrieve data by severity: <v2:AlarmCriteria xmlns:v2="http://www.axeda.com/services/v2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">    <v2:severity xsi:type="v2:GreaterThanEqualToNumericQuery">     <v2:value>900</v2:value>   </v2:severity>   <v2:states/> </v2:AlarmCriteria>   Or in a custom object: import static com.axeda.sdk.v2.dsl.Bridges.* import com.axeda.services.v2.* import com.axeda.sdk.v2.exception.*   def q = new com.axeda.services.v2.GreaterThanEqualToNumericQuery() q.value = 900 ac = new AlarmCriteria(severity:q) aresults = alarmBridge.find(ac)   Currently the Query Types do not map properly in JSON objects - use XML to perform these types of queries via the REST APIs. References: Axeda v2 API/Services Developer's Reference Guide 6.6 Axeda Platform Web Services Developer Reference v2 REST 6.6 Axeda v2 API/Services Developer's Reference Guide 6.8 Axeda Platform Web Services Developer Reference v2 REST 6.8
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Load Testing through C SDK Remote Device Simulation in ThingWorx   As discussed in the EDC's previous article, load or stress testing a ThingWorx application is very important to the application development process and comes highly recommended by PTC best practices. This article will show how to do stress testing using the ThingWorx C SDK at the Edge side. Attached to this article is a download containing a generic C SDK application and accompanying simulator software written in python. This article will discuss how to unpack everything and move it to the right location on a Linux machine (Ubuntu 16.04 was used in this tutorial and sudo privileges will be necessary). To make this a true test of the Edge software, modify the C SDK code provided or substitute in any custom code used in the Edge devices which connect to the actual application.   It is assumed that ThingWorx is already installed and configured correctly. Anaconda will be downloaded and installed as a part of this tutorial. Note that the simulator only logs at the "error" level on the SDK side, and the data log has been disabled entirely to save resources. For any questions on this tutorial, reach out to the author Desheng Xu from the EDC team (@DeShengXu).   Background: Within ThingWorx, most things represent remote devices located at the Edge. These are pieces of physical equipment which are out in the field and which connect and transmit information to the ThingWorx Platform. Each remote device can have many properties, which can be bound to local properties. In the image below, the example property "Pressure" is bound to the local property "Pressure". The last column indicates whether the property value should be stored in a time series database when the value changes. Only "Pressure" and "TotalFlow" are stored in this way.  A good stress test will have many properties receiving updates simultaneously, so for this test, more properties will be added. An example shown here has 5 integers, 3 numbers, 2 strings, and 1 sin signal property.   Installation: Download Python 3 if it isn't already installed Download Anaconda version 5.2 Sometimes managing multiple Python environments is hard on Linux, especially in Ubuntu and when using an Azure VM. Anaconda is a very convenient way to manage it. Some commands which may help to download Anaconda are provided here, but this is not a comprehensive tutorial for Anaconda installation and configuration. Download Anaconda curl -O https://repo.anaconda.com/archive/Anaconda3-5.2.0-Linux-x86_64.sh  Install Anaconda (this may take 10+ minutes, depending on the hardware and network specifications) bash Anaconda3-5.2.0-Linux-x86_64.sh​ To activate the Anaconda installation, load the new PATH environment variable which was added by the Anaconda installer into the current shell session with the following command: source ~/.bashrc​ Create an environment for stress testing. Let's name this environment as "stress" conda create -n stress python=3.7​ Activate "stress" environment every time you need to use simulator.py source activate stress​  Install the required Python modules Certain modules are needed in the Python environment in order to run the simulator.py  file: psutil, requests. Use the following commands to install these (if using Anaconda as installed above): conda install -n stress -c anaconda psutil conda install -n stress -c anaconda requests​ Unpack the download attached here called csim.zip Unzip csim.zip  and move it into the /opt  folder (if another folder is used, remember to change the page in the simulator.json  file later) Assign your current user full access to this folder (this command assumes the current user is called ubuntu ) : sudo chown -R ubuntu:ubuntu /opt/csim   Move the C SDK source folder to the lib  folder Use the following command:  sudo mv /opt/csim/csdkbuild/libtwCSdk.so.2.2.4 /usr/lib​ You may have to also grant a+x permissions to all files in this folder Update the configuration file for the simulator Open /opt/csim/simulator.json  (or whatever path is used instead) Edit this file to meet your environment needs, based on the information below Familiarize yourself with the simulator.py file and its options Use the following command to get option information: python simulator.py --help​   Set-Up Test Scenario: Plan your test Each simulator instance will have 8 remote properties by default (as shown in the picture in the Background section). More properties can be added for stress test purposes in the simulator.json  file. For the simulator to run 1k writes per second to a time series database, use the following configuration information (note that for this test, a machine with 4 cores and 16G of memory was used. Greater hardware specifications may be required for a larger test): Forget about the default 8 properties, which have random update patterns and result in difficult results to check later. Instead, create "canary properties" for each thing (where canary refers to the nature of a thing to notify others of danger, in the same way canaries were used in mine shafts) Add 25 properties for each thing: 10 integer properties 5 number properties 5 string properties 5 sin properties (signals) Set the scan rate to 5000 ms, making it so that each of these 25 properties will update every 5 seconds. To get a writes per second rate of 1k, we therefore need 200 devices in total, which is specified by the start and end number lines of the configuration file The simulator.json  file should look like this: Canary_Int: 10 Canary_Num: 5 Canary_Str: 5 Canary_Sin: 5 Start_Number: 1 End_Number: 200​ Run the simulator Enter the /opt/csim  folder, and execute the following command: python simulator.py ./simulator.json -i​ You should be able to see a screen like this: Go to ThingWorx to check if there is a dummy thing (under Remote Things in the Monitoring section): This indicates that the simulator is running correctly and connected to ThingWorx Create a Value Stream and point it at the target database Create a new thing and call it "SimulatorDummyThing" Once this is created successfully and saved, a message should pop up to say that the device was successfully connected Bind the remote properties to the new thing Click the "Properties and Alerts" tab Click "Manage Bindings" Click "Add all properties" Click "Done" and then "Save" The properties should begin updating immediately (every 5 seconds), so click "Refresh" to check Create a Thing Template from this thing Click the "More" drop-down and select "Create ThingTemplate" Give the template a name (ensure it matches what is defined in the simulator.json  file) and save it Go back and delete the dummy thing created in Step 4, as now we no longer need it Clean up the simulator Use the following command: python simulator.py ./simulator.json -k​ Output will look like this: Create 200 things in ThingWorx for the stress test Verify the information in the simulator.json  file (especially the start and end numbers) is correct Use the following command to create all things: python simulator.py ./simulator.json -c​ The output will look like this: Verify the things have also been created in ThingWorx: Now you are ready for the stress test   Run Stress Test: Use the following command to start your test: python simulator.py ./simulator.json -l​ or python simulator.py ./simulator.json --launch The output in the simulator will look like this: Monitor the Value Stream writing status in the Monitoring section of ThingWorx:   Stop and Clean Up: Use the following command to stop running all instances: python simulator.py ./simulator -k​ If you want to clean up all created dummy things, then use this command: python simulator.py ./simulator -d​ To re-initiate the test at a later date, just repeat the steps in the "Run Stress Test" section above, or re-configure the test by reviewing the steps in the "Set-Up Test Scenario" section   That concludes the tutorial on how to use the C SDK in a stress or load test of a ThingWorx application. Be sure to modify the created Thing Template (created in step 6 of the "Set-Up Test Scenario" section) with any business logic required, for instance events and alerts, to ensure a proper test of the application. 
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Background: Firewall-Friendly Agents can be configured for server certificate authentication in the Axeda Builder project or via the Axeda Deployment Utility. When server certificate authentication is configured, the Agent will compare the certificate chain sent by the Platform to a local copy of the CA certificate chain stored in the SSLCACert.pem file in the Agent’s home directory. The certificate validation compares three things: Does the name of the Platform certificate match the name in the request? Does the CA certificate match the CA certificate that signed the Platform certificate? Is the Platform or CA certificate not expired? If the answer to any of these questions is “no”, then connection is refused and the Agent does not communicate further with the Platform. To determine if certificate trouble is an issue, see the Agent log: EKernel.log or xGate.log. Recommendation: For Agent-Platform communications, we recommend always using SSL/HTTPS. If the Agent is not configured to validate the server certificate (via the trusted CA certificate), the system is vulnerable to a number of security attacks, including “man in the middle” attacks. This is critically important from a security perspective. Note: For on-premise customers, if the Platform certificate needs to change, always update the SSLCACert.pem file on all Agents before updating the Platform certificate. (If the certificate is changed on the Platform before it is changed on the Agents, communications from the Agent will stop.) Note: Axeda ODC automatically notifies on-demand customers about any certificate updates and renewals. At this point, though, Axeda ODC certificate updates are not scheduled for several years. Finally, it is recommended that your Axeda Builder project always specify “Validate Server Certificate” and set the encryption level to the strongest level supported by the Web server. Axeda recommends 168 bit encryption, which will use one of the following encryption ciphers: AES256-SHA or DES-CBC3-SHA. Need more information? For information about configuring and managing Agent certificate authentication, see Using SSL with Axeda® Platform Guide.
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Background: In the event that a Gateway/Connector Agent is offline or unable to connect to the Axeda Cloud Server, it uses an internal message queue to store information until the connection is restored. The message queue size is configured in the Axeda Builder project. By default, the queue is 200KB in size. Depending on how frequently your Agent sends data or how much data your Agent is collecting and trying to send, 200KB may be too small.  If the queue is too small, the data will “overflow” the queue. The queue is kept in memory only; data is not stored to disk and will be removed in a First-In-First-Out (FIFO) manner when the queue overflows. If you see queue overflow error messages in the Agent log (either EKernel.log and xGate.log), it may be time to change the size of the outbound message queue. The correct size setting for the Agent outbound message queue takes three variables into consideration: How much information you are sending? What is the maximum expected duration for loss of connection to the Internet (Cloud Server)? How much memory is available for your process? The more information the Agent is trying to send, the larger the queue size setting should be. Consider also that if your Agents are offline (disconnected) for a long period of time, they will likely accumulate lots of data, which may overflow the outbound message queue. If this is the case, you’ll need to increase the queue or risk losing data. Recommendation: Consider how the Agent operates (offline/online data collection) and how much data may be queued. When selecting the size of the queue, it’s important to maintain a balance between protecting against data loss and not occupying too much memory. If you do determine that you need to increase the outbound message queue size based, it’s important to note that Axeda recommends a maximum size of the outbound message queue of about 2MB. Need more information? For information about specifying Agent outbound message queue size, see the online help in Axeda® Builder (Enterprise Server Settings). For information about how the Agent delivers data to the Platform (via EEnterpriseProxy/xgEnterpriseProxy), see the Agent user’s guide for your Agent: either Axeda® Platform Axeda® Gateway User’s Guide (PDF) or Axeda® Platform Axeda® Connector User’s Guide (PDF). Axeda Support Site links: Axeda® Gateway User’s Guide, Axeda® Connector User’s Guide.
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Load Testing through Remote Device Simulation   Designing an enterprise-ready application requires extensive testing and quality assurance. This includes all sorts of tests, of course, from examining the user interface for flaws to verifying there is correct logic in all background services. However, no area of testing is more important than scalability. Load testing is how to test the application to ensure it still functions as desired when remote things are connected and streaming information to the Platform.   Load testing is considered a critical component of the change management process. It is mentioned numerous times throughout PTC best practice documentation. This tutorial will step you through designing a load test using Kepware as a simulator. Kepware is free to download and use in short demos, making it the perfect tool for this type of test.   Start by acquiring the latest version of Kepware from the download site. Click “Download Free Demo” if a license was not included in your PTC product package. The installation of Kepware is simple, and for details, see the Kepware Installation Guide. The tutorial shown here uses Kepware version 6.7 and ThingWorx version 8.4.4. Given that we are testing a ThingWorx application, this tutorial assumes ThingWorx is already installed and configured correctly.   Once Kepware is installed, follow these steps: (This tutorial was developed by Desheng Xu and edited by Victoria Tielebein. Exact specifications of the equipment used in both large scale and local tests are given in step VI, which discusses the size of the simulation)   Understand how to configure Kepware as a simulator Go to the Help menu within Kepware, and click on “Driver Help” Select “Simulator” in the pop-up window, and click “OK” Expand “Address Descriptions” and then “Simulation Functions” Select “Ramp Function” to review details about the function needed for this tutorial, as well as information about function syntax Close the window once this information has been reviewed Create a new project in Kepware Click “File” > “New” In case you are connected to runtime, Kepware will allow you to choose to edit this project offline Add a channel in Kepware Channels represent threads which Kepware will use to contact ThingWorx Under “Connectivity”, click “Click to add a channel.” From the drop-down list, select “Simulator” Use all the default settings, selecting “Next” all the way down to “Finish” Next, add one device to the channel Highlight the new channel and click “Click to add a device” (which will appear in the center of the screen) Once again, use the default settings, selecting “Next” all the way down to “Finish” Add a tag to this device Within Kepware, tags represent properties which bind to remote things on the Platform and update with new information over time. Each device will need several tags to simulate remote property updates. The easiest way to add many tags for testing is to create one, and then copy and paste it. Highlight the device created in the previous step and click “Click to add static tag”, which appears in the center of the screen For “name” type “tag1” For Address, enter the Ramp function: RAMP(1000,1,2000000,1) The first parameter is the update rate given in milliseconds The next two parameters are the range of values which can be sent The last parameter is the increment or step Together this means that every 1 second, this tag will send a new value that is 1 higher than the previous value to the Platform, starting at 1 and ending at 2 million Ensure the Data Type is given as “DWord” or any type which will be read as a “Number” (and NOT an “Integer”) on the Platform Change the Scan Rate to 250 Then click “OK” Add more devices to the test The most basic set-up is now done: if this project connected to the Platform, one remote thing with one remote property could be used to simulate property updates. That is not very useful for load testing, however. We need many more things than this, and many more properties. The number of tags on each device should match the expected number of remote properties in the application itself. The number of devices in each channel should be large enough that when more channels are created, the number of total devices is close to the target for the application. For example, to simulate 10,000 things, each with 25 remote properties, we need 25 tags per device, 200 devices per channel, and 50 channels. This would require a lot of memory to run and should not be attempted on a local machine. A full test of 40 channels each with 10 devices was performed as shown in the screenshots here. This simulates 10,000 writes per second to the Platform total, or about 400 remote device connections. This test used the following hardware specifications: Kepware machine running Windows 2016 64-bit, 2 cores, 8G ThingWorx Platform machine running Ubuntu 16.04, 4 cores, 16G PostgreSQL 9.6 machine running Ubuntu 16.04, 4 cores, 16G Influx 1.6.3 machine running Ubuntu 16.04, 4 cores, 16G A local test was also run on Windows 10 (64-bit), using the H2 database, with Kepware and ThingWorx running side by side on the machine, 4 cores, 16G. This test made use of only 2 channels, with 10 devices each. For local tests to see how the simulation works, this is fine, but a more robust set-up like the above will be needed in a true load test. If there is not enough memory on the machine hosting Kepware, errors like this will appear in the Kepware logs: One or more value change updates lost due to insufficient space in the connection buffer. Once you decide on the number of tags and devices needed, follow the steps below to add them.  To add more tags, copy and paste the existing tag (ctrl+c  and ctrl+v  work in Kepware for convenience) until there as many tags as desired To add more devices, highlight the device in Kepware and copy and paste it as well (click on the channel before pasting) Then, copy and paste the entire channel until the number of channels, devices, and tags totals the desired load (be sure to click on “Connectivity” before hitting paste this time)  Configure the ThingWorx connection Right click on Project in the left-hand navigation bar and in the pop-up window that appears, highlight ThingWorx Change the “Enable” field to “Yes” to activate the other fields Fill in the details for “Host”, “Port”, “Application Key”, and “Thing name” Note that the application key will need to be created in ThingWorx and then the value copied in here The certificate and encryption settings may also need to be adjusted to match your environment For local set-ups, it is likely that self-signed and all certificates will need to be accepted, so both of those fields will likely need to be set to “Yes” (Encryption may need to be disabled as well). In production systems, this should not be the case  Save the project It doesn’t matter too much if this project is saved as encrypted or not, so either enter a password to encrypt the save or select “No encryption” Connect to ThingWorx Click “Runtime” > “Connect…” A pop-up will appear asking if you want to load this project, click “Yes” The connection status should then appear in the bottom portion of the window where the logs are displayed Configure in ThingWorx Login to the ThingWorx Platform Under “Industrial Connections” a thing should appear which is named as indicated in the Kepware configuration step above Click to open this thing and save it Also create a new thing, a value stream for ingesting data from Kepware Create remote things in ThingWorx Import the provided entity into ThingWorx (should appear as a downloadable attachment to this post) Open the KepwareUtil thing and go to the services tab Run the AutoKepwareCreate service to generate remote things on the Platform Give the name of the stream created above so each thing has a place to store property information The IgnoreTemplate flag should be set to false. This allows for the service to create a thing template first, which is then passed to the remote devices. The only reason this would be set to true is if the devices need to be deleted and recreated, but the template does not (then set the flag to true). To delete the devices, use the AutoKepwareDelete service also provided on the KepwareUtil thing Note that the AutoKepwareCreate service is asynchronous, so once it is executed, close the window and check the script logs to see when it completes. The logs will look like: KepwareUtil AutoKepwareCreate task finished!!! Check status of remote things Once the things are created, they should automatically connect to the Platform Run the TotalDeviceByTemplateWithTemplate service to see if the things are connected The template given here could be the one created by the AutoKepwareCreate service, or just give it RemoteThings if this is a small local set-up without many remote things on it The number of devices will equal the number of devices per channel times the total number of channels, which in the test shown here, is 400 isConnected will be checked if all of the devices are connected without issue If some of them are not connected, verify in the logs if there are any errors and resolve those before moving on View Ingestion Rate Once the devices are created, their tags should show as numbers (NOT integers), and they should already be updating with new values every second To view the ingestion rate, run the KepwareUtil service AutoKepwareRateSummary Give the thing template name that is created by the AutoKepwareCreate service, which will look like the name of the Kepware thing itself with a “T-“ in the front The start time should be close to the current time, and the periodInMinutes should be large enough to include some of the test (periodInMinutes is used to calculate the end time within the service) Note in the results here that the Average Write Per Second is only 9975 wps, which is close but not exactly what we would expect. This means that there are properties not updating correctly, which requires us to look at the logs and restart some things. If nothing shows up here, despite the Total Connected Things showing correctly, then look at the type of the tags on one of the remote devices. The type must be NUMBER for the query within this service to work, and not INTEGER. If the type of the tags is incorrect, then the type of the tags within Kepware was probably given as something which is not interpreted as a number in ThingWorx. Ensure DWord is used for the tags in Kepware Within the script log, look for any devices which show errors as seen in the image below and restart them to get their properties updating correctly Once the ingestion rate equals what is expected (in the case of the test here, 10,000 wps), use the AutoKepwareIngestionStat service on the KepwareUtil thing to see details about each remote device The TimeGapAvg in this service represents the gap between two ingestions in milliseconds, showing any lag that may be present between Kepware and ThingWorx The TimeGapSTD shows the standard deviation of the time gap between two ingestions on any given thing, also indicating lag (the lower this number, the better) The StartTime and EndTime show the first and last timestamp observed in the ThingWorx database during the given duration The totalCount shows the total number of ingested records during the sampling cycle The StartValue and EndValue fields show the first and last value ingested into the tag during the given duration If the ingestion rate is working as expected, and the ramp function is actually sending an update on time (in this case, once each second), then the difference between the EndValue and StartValue should always be equal to the totalCount plus 1. If this doesn’t match up, then there may be data loss or something else wrong with the property updates, which will show as a checked box in the valueException column. It is not enough to ensure that the ingestion rate is correct, as sometimes the rate may fluctuate only by 1 or 2 wps and appear perfect, even while some data is lost. That is why it is important to ensure that there are no valueException boxes showing as checked in the test of the application. If none of these are marked as having failed, then the test was successful and this ingestion rate is acceptable for the application   This tutorial is a very basic way to simulate many remote devices ingesting data into the Platform. For this to be a true test of the application, the remote things created in this test will need to be given business logic tasks as well. The AutoKepwareCreate service can be modified to give any template (and not just RemoteThing) to the thing template which is created and subsequently passed into the demo devices. Likewise, the template itself can be created, and then manually modified to look like the actual remote device template in the application, before the rest of the things are created (using the IgnoreTemplate flag in the creation and deletion services, as discussed above).   Ensure that events are triggered as expected and that subscriptions to property updates are in place on the thing template before creating the demo things. Make use of the subsystem monitor to ensure that the event, value stream, and stream queues do not grow so large that the Platform cannot keep up with the requests (for details about tuning the stream and value stream processing subsystems, see PTC’s best practice documentation). Also be sure to load some of the mashups to see how they perform while the ingestion test is happening. This will test whether or not the ingestion rate and business logic of the application can function side by side without errors, data loss, or performance issues.
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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      
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Background: Axeda Agents can be configured with standard drivers to collect event-driven data, which is then sent to the Platform. Axeda provides many standard event-driven data (EDD) drivers for use with the Axeda Agent (as explained in Axeda® Agents EDD Toolkit Reference (PDF)). All EDD  drivers are configured by an xml file and enabled in Axeda Builder, through the Agent Data Items configuration. You can configure an EDD driver to send important information from your process to the Agent, including data items, events and alarms. The manner in which you configure your drivers will affect the ability for your project to operate efficiently. Recommendation: Use drivers to reduce the amount of data sent to the platform. Instead of sending data items to the Platform, which then generates an event or alarm, it is possible to use the drivers to scan for specific data points or conditions and send an event or alarm. Before you can configure your agents, you first need to determine how often you will need your agent to send data to the Enterprise server. Two example workflows and recommendations: If you want to monitor a data item every second or two, configure the Agent to do the monitoring If you want to trend information once per day, perform that logic at the Enterprise Server. These examples may address your actual use case or your needs may fall somewhere in between. Ultimately, you want to consider that time scale (how often you want to monitor or trend data) and resulting data volume should drive how your system handles data. More data is available at the Agent, and at a higher frequency, then that needed at the Platform. Processing at the Agent ensures that only the important results are communicated to the Platform, leading to a “cleaner” experience for the Platform. Using this guidance as a best practice will help reduce network traffic for your customers as well as ensure the best experience for Enterprise users using server data in their dashboards, reports, and custom applications. Need more information? For information about the standard EDD Drivers, see the Axeda® Agents: EDD Drivers Reference (PDF).
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    We’re back with a ThingWorx 8.5 highlight! Today, we’ll cover brand-new functionality within ThingWorx Flow—the Azure Connector!   Imagine you’re on the regal shores of ThingWorx, trying to reach the plentiful utopia of Azure IoT services and capabilities. How do you get there? Through the new Azure Connector for ThingWorx Flow!   The Azure Connector for ThingWorx Flow is an OOTB connector that enables you to leverage the power and breadth of Azure services directly in ThingWorx applications with ThingWorx Flow. If you have intermittent needs for high-scale processing, for example, the Azure connector can help optimize costs based on when you require a mighty powerful liege and where you only pay for the necessary processing capacity when it is important. Azure Functions are also useful when you want to leverage some special code that was written in a language like Python that you’d like to easily leverage, such as to pre-process information collected from a device.   With the Azure connector, you’re able to: Support execution of logic apps to access a large number of OOTB systems connectors and customer investments in apps. Enable execution of Azure functions wherever elastic scale is needed to cost-effectively support use cases that have intermittent high-scale needs, such as executing an analysis of device sensors leading up to and following a key alert or incident. Enable use of Azure Cognitive Services to easily leverage a rich set of capabilities from simple text to voice or voice to text to computer vision in end-to-end IoT use cases. A few examples include the ability to: Convert the alert text to speech to deliver to an operator to take urgent action. Convert a verbal description of an assembly problem to a textual description in an automatically generated problem report. Use computer vision for a quality check to automatically pass or fail a part based on a visual image and confidence level. Here are a few of the many different services you can leverage with the Azure Connector in ThingWorx Flow: Anomaly Detector Bing Search Computer Vision Custom Vision Execute Function Execute Logic App Face Recognition LUIS Prediction QnA Prediction Speaker Recognition Speech Service Text Analytics Check out this demo video to see an example in action. In the video, you’ll see us: Create an Azure Function app. Create a ThingWorx Flow that triggers from an alert on a ThingWorx thing. Within the flow, you’ll see us consume the alert information and send it to the Azure Function app, and the Azure function app will process and return a response. (view in My Videos)   While simple, this demo shows the ability to connect ThingWorx and Azure together for an incredibly flexible and powerful distributed IoT system.   Finally, if you’re looking for assistance on how to use the new connector, see our Help Center here.   Stay connected and go with the (ThingWorx) Flow! Kaya        
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The RabbitMQ Management plugin provides a web-based interface into the inner workings of the messaging bus behind ThingWorx Flow. It is installed by the Flow installers but is an HTTP service by default and is a totally different web server than the NGINX used to front-end ThingWorx Flow. This will describe how to integrate it into the NGINX on your ThingWorx Flow server. This is necessitated by some recent browser behavior changes that make it very hard to get to the http port once you've used an https service on the same machine from the same browser.   First - let's find the user name and password for the RabbitMQ Management plugin. On a Linux server, the file /etc/rabbitmq/definitions.json will hold the name and password for the plugin's UI:         "users": [{                 "name": "flowuser",                 "password": "1780edc6b8628ace2ace72465cdc7b048c88",                 "tags": "administrator"         }],   On a Windows server, the definitions.json file can be found under [flow install location]\modules\RabbitMQ.   Of course, access to these directories should be limited.   Second - let's integrate the plugin into NGINX The best way to integrate the plugin into Flow is to let NGINX reverse proxy to the other http server running the UI for the plugin, which is exactly what happens for Thingworx itself. That way, only NGINX has to be configured for https and no other ports need to be opened to allow access to the plugin.   You need to find the file vhost-flow.conf on your system. On Linux, this will be /etc/nginx/conf.d/vhost-flow.conf. On Windows, it will be at C:\Program Files\nginx-[version]\conf\conf.d\vhost-flow.conf by default. Add the following fragment after the last location xxx {…} segment in the file:       # deal with the rabbitMQ admin tool     location ~* /rabbitmq/api/(.*?)/(.*) {         proxy_pass http://127.0.0.1:15672/api/$1/%2F/$2?$query_string;         proxy_buffering                    off;         proxy_set_header Host              $http_host;         proxy_set_header X-Real-IP         $remote_addr;         proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;         proxy_set_header X-Forwarded-Proto $scheme;     }       location /rabbitmq {         rewrite ^/rabbitmq$ /rabbitmq/ permanent;      }       location ~* /rabbitmq/(.*) {         rewrite ^/rabbitmq/(.*)$ /$1 break;         proxy_pass http://127.0.0.1:15672;         proxy_buffering                    off;         proxy_set_header Host              $http_host;         proxy_set_header X-Real-IP         $remote_addr;         proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;         proxy_set_header X-Forwarded-Proto $scheme;     }   This makes the request for /rabbitmq get pushed over to the web server at port 15672 on the Flow server.   Test the updated config file with (nginx may not exist in your normal path): nginx -t   Restart the NGINX service: Linux (one of these will work depending upon your Linux version): systemctl restart nginx service nginx restart Windows: Net stop ThingWorxOrchestrationNginx Net start ThingWorxOrchestrationNginx -or- use the Services app to restart the service   Thanks to https://groups.google.com/forum/#!topic/rabbitmq-users/l_IxtiXeZC8 for the needed config changes.   You can now use https://yourserver/rabbitmq to get to the login page for the management plugin. Login with the user and password from the definitions.json file on your system and you can now monitor the behavior of your RabbitMQ environment.
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Background: In very rare situations, it is possible that the Firewall-Friendly Agent process may stop running. If the Agent is not running, no machine monitoring or communication with the Cloud Server is possible. Recommendation: WatchDog is a little known yet very helpful feature available with Firewall-Friendly Agents. This program lets you monitor whether an Agent is running; if it’s not running, WatchDog can restart that Agent if needed. If the Agent process fails, WatchDog can bring it back up! WatchDog can also be configured to watch other processes. You can configure WatchDog to run as a service (for Windows) or daemon (for Linux). You will register the Watchdog to run as a service.  The Watchdog configuration file will specify the process(es) to be monitored and what to do when one exits. The options are to attempt to restart the process or to restart the system. Note: Watchdog detects only if a watched process exits. It will not detect or report on processes that may be “hanging”. Need more information? For information about configuring and using WatchDog, see the Agent User’s Guide for your Agent: either Axeda® Platform Axeda® Gateway User’s Guide (PDF) or Axeda® Platform Axeda® Connector User’s Guide (PDF).
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Having trouble remembering how to get into Flow? How about make /Flow the URL?   Since the Flow environment uses NGINX to front-end the various components that make up Flow, there is a very sophisticated set of rewrites and proxy_pass directives in the NGINX configuration. All you have to do is add another 'location' fragment to the vhost-flow.conf file that will push /Flow over to /Thingworx/Composer/apps/flow:       location /Flow {       rewrite ^/Flow$ $proxy_scheme://$server_host/Thingworx/Composer/apps/flow permanent;     }   On Linux, the file should be at /etc/nginx/conf.d/vhost-flow.conf   On Windows, the file should be at c:\Program Files\nginx-[version]\conf\conf.d\vhost-flow.conf   Test the updated config file with (nginx may not exist in your normal path): nginx -t   Restart the NGINX service: Linux (one of these will work depending upon your Linux version): systemctl restart nginx service nginx restart Windows: Net stop ThingWorxOrchestrationNginx Net start ThingWorxOrchestrationNginx -or- use the Services app to restart the service   From this point forward https://yourserver/Flow will take you to ThingWorx Flow's home page.
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Background: Customer machines create files containing data, configuration, log data, etc. These files may contain critical information for service technicians about the asset and its data. How can you make sure you get this important information to the Cloud as soon as it’s available? Use File Watcher, a standard tool available with Firewall-Friendly Agents. You can configure File Watcher to monitor a directory or file specification and automatically upload the file(s) to the Axeda Cloud. By monitoring selected directories and files on the device, the File Watcher determines which files have changed or appeared and then upload those files to the Axeda Cloud Server. Sometimes the size of the file being written or copied into the file watch target is large enough that it takes time to complete. Without taking the right precautions, the file upload may try to start before the file creation process is completed. Recommendations: To ensure that large files don’t cause problems, we recommend using a “move” or “rename” operation instead of a file copy operation to place your file into a watched directory. Unlike a “copy” operation, using autonomous operations like move or rename will ensure that the Agent doesn’t detect a file is in the midst of growing and prematurely begin to upload that operation. In situations when it isn’t possible to use the autonomous “move” or “rename” operations, you can implement a delay. Setting a delay in the File Watcher configuration will prevent the Agent from sending files to the Axeda CloudServer before those files have transferred completely to the watched file directory. Finally, use file compression wisely. You need to balance the benefits from compressing files before sending with the potential adverse impact that compression will have on the Agent. Compressing very large files BEFORE sending to the Platform will affect the Agent; however, compressing smaller files can be beneficial. If the Agent’s computer is of lower power, the CPU may have to work overtime to compress huge files or to send huge uncompressed files. As a general rule, compressing files before sending is recommended. However, depending on your Agent and network setup, it may prove more beneficial to stream uncompressed files rather than compress first and then send. Need more information? For information about configuring and using file watchers, see the Axeda® Builder User’s Guide (PDF) or the online help in Axeda Builder.
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I'm getting up to speed on all the great new stuff in 8.5, and have found that since the JavaScript engine was upgraded to Rhino 1.7.11, there's some awesome new JavaScript ES6 functionality available. I have tested arrow functions, filter, map, and reduce. Compose does not look like it is supported.   If you're not familiar with this functionality, I highly recommend reading up on them. Filter, map, and reduce are incredibly useful for working with arrays. They can save you a lot of annoying logic.   Here's some resources that I've found helpful for learning: JavaScript Functional Programming - map, filter and reduce Arrow Functions: Fat and Concise Syntax in Javascript If you really want to dive into ES6, Wes Bos has incredible tutorial sessions that are worth every penny: Wes Bos: ES6 for Everyone!   Have you played around with ES6 functionality in ThingWorx 8.5 yet?
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Hi,   I launched version 2.1.0 of the ThingWorx GitBackup Extension on Github, in the following repository: https://github.com/vrosu/thingworx-gitbackup-extension This release contains a few UI fixes and experimental support for proxy.   "The GitBackup extension is updated on a best-effort basis and accepts community fixes, that are best done through GitHub. Please report any troubles via GitHub's Issue system.   Important notice: As you might be aware, we have just launched ThingWorx 8.5 and with it a new solution called Solution Central, which is a brand-new cloud-based service to help you package, store, deploy, and manage your ThingWorx applications.   This service is supported by PTC and it contains features that seem to be similar to the GitBackup extension, but they are not. They are different type of tools: Git is a source versioning tool, while Solution Central is an artifact repository. When deploying applications between ThingWorx instances you should use Solution Central, while if you want to version your entities during development you can use the GitBackup extension"  
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