cancel
Showing results for 
Search instead for 
Did you mean: 
cancel
Showing results for 
Search instead for 
Did you mean: 

ThingWorx Navigate is now Windchill Navigate Learn More

IoT & Connectivity Tips

Sort by:
While it is not a requirement, it is a best practice to install KEPServerEX (v6.2 or higher) before installing ThingWorx (v8.0.1 or higher). If ThingWorx is already installed, close the application and complete the install of KEPServerEX by following these install instructions: How do I download and install KEPServerEX? Now, when you attempt to launch ThingWorx, if you are presented with a "null pointer exception" error, follow this workaround: 1. Navigate to the 'PostgreSQL\installer' directory, within the directory where the Manufacturing Apps are installed. By default this will be: <ThingWorx install path>\ThingWorxManufacturingApps\PostgreSQL\installer 2. Run the 'vcredist.exe' located there. This application should re-install the conflicting redistributables, and you should be able to launch ThingWorx again normally.
View full tip
The .NET Framework versions 3.5 and 4.5 are required external dependencies. The ThingWorx Manufacturing Apps installer will prompt you to install these. For more information on the System requirements and External dependencies, see: Not authorized to view the specified document 3992 If you are a previous ThingWorx user, and are not using the full ThingWorx Manufacturing Apps installer, you will need to add these framework versions to the Windows operating system. An installer is available from the PTC App store for this purpose, or this can be done manually by following the instructions below. Note: both the .NET 3.5 and .NET 4.5 Frameworks are required. Do not install the .NET 3.5 Framework alone. Manually add the .NET 3.5/4.5 Framework to Windows: 1. Open the Windows Control Panel | select Programs and Features. 2. Select the option to "Turn Windows features on or off" 3. In Windows 7 (or other Standard editions), you can select the .NET framework from the list and follow the steps for installation. 4. In Windows Server 2012 (and other Server editions), select the option to "Add roles and features," then Next. For Installation type, select "Role-based or feature-based installation," then Next. Choose your server from the Server Selection menu, then Next. Then choose the framework versions and complete the install.
View full tip
The ThingWorx Manufacturing Apps 8.0.1 are tested and supported for use with ThingWorx 8.0.1. The ThingWorx Manufacturing Apps 8.1.0 are tested and supported for use with ThingWorx 8.1.0.
View full tip
Platform Support Windows Server 2008 R2 SP1, Windows 2012 R2, and Cent OS 7.1 (paid version only) are recommended and fully tested for production. Server Support • KEPServerEX v6.2, which includes the ThingWorx Native Interface. Note: Non-Kepware OPC Servers servers and earlier versions of KEPServerEX can be connected to KEPServerEX v6.2, functioning as an aggregator (OPC UA Server). KEPServerEX and ThingWorx can be installed on the same machine. However-- for production-- separate machines are recommended. • ThingWorx 8.0 with PostgresSQL 9.4.10-1 database, Express • ThingWorx 8.0, with the ThingWorx Manufacturing Apps imported as a ThingWorx extension Minimum recommended hardware • OS — Windows 2008R2 • SP1 / Windows 2012R2 • Disk Space — 100 GB • RAM — 7 GB • CPU — 3 Core Client Browser Support - Paid Version • Chrome 44 • Firefox 35+ • Safari 6.1.6+ • Internet Explorer 11+ For more information on the installation requirements, see the Product Requirements section of the install guide here: Not authorized to view the specified document 3992
View full tip
KEPServerEX requires the 32-bit version of Java if you are using the IoT Gateway Plug-in. If you do not have the 32-bit version installed and attempt to connect the IoT Gateway, the KEPServerEX Event Log will report the following error: “IoT Gateway failed to start, 32-bit JRE required." Some of the Manufacturing Applications training content relies on this Plug-in, as well. As a best practice, it is recommended that both the 32-bit and 64-bit versions of Java be installed. This install is available for download from the Oracle website, here: Java SE Runtime Environment 8 - Downloads
View full tip
Users can install ThingWorx and KEPServerEX on the same PC or server. It is recommended to install KEPServerEX prior to installing ThingWorx in this case. For more information, see: Do I need to uninstall ThingWorx before installing KEPServerEX?
View full tip
Many users of our software have submitted cases regarding the Third-Party Components and their functions within ThingWorx Analytics. This short blog post will provide the main components used by our software and explain their functionality.   ThingWorx Analytics uses the following components in its default installation:   Apache ZooKeeper ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services ThingWorx Analytics uses ZooKeeper as the gatekeeper to API calls and processes to the Application Component Homepage: https://zookeeper.apache.org/ Apache Tomcat ​Apache Tomcat software is an open source implementation of the Java Servlet, JavaServer Pages, Java Expression Language and Java WebSocket technologies ThingWorx Analytics uses Tomcat to handle webservices and API communications This enables the use of ThingWorx Foundation (Core) mashups with ThingWorx Analytics Server Component Homepage: http://tomcat.apache.org/ PostgreSQL Server PostgreSQL is an open source object-relational database system ThingWorx Analytics uses PostgresSQL server to store analytical results for later retrieval Component Homepage: https://www.postgresql.org/
View full tip
This has been moved to its new home in the Augmented Reality Category in the PTC Community.
View full tip
When you install Thingworx with PostgreSQL, you can't import the "PostgreSQL" extension because of the conflict of a library file. So, here is a sample "MetaData.xml" file. You can zip this file and simply import it into your Thingworx so that you can have a thing template for "PostgreSQL" database. <Entities>     <ExtensionPackages>         <ExtensionPackage name="PostgreSQL_ExtensionPackage"                       description="PostgreSQL JDBC Extension"                       vendor="ThingWorx Customer Service"                       packageVersion="1.0"                       minimumThingWorxVersion="4.0.0">         </ExtensionPackage>     </ExtensionPackages>     <ThingTemplates>         <ThingTemplate baseThingTemplate="Database" description="PostgreSQL Server" documentationContent="" effectiveThingPackage="" homeMashup="" lastModifiedDate="2015-11-28T11:40:35.355-05:00" name="PostgreSqlServer" tags="" thingPackage="">             <ThingShape description="" documentationContent="" lastModifiedDate="2015-11-28T11:40:35.355-05:00" name="" tags="">                 <PropertyDefinitions/>                 <ServiceDefinitions/>                 <EventDefinitions/>                 <ServiceImplementations/>                 <ServiceMappings/>                 <Subscriptions/>             </ThingShape>             <ImplementedShapes/>             <ConfigurationTables>                 <ConfigurationTable description="" isMultiRow="false" name="ConnectionInfo">                     <DataShape>                         <FieldDefinitions>                             <FieldDefinition aspect.defaultValue="5.0" baseType="NUMBER" description="Maximum number of connections in the pool" name="maxConnections" ordinal="0"/>                             <FieldDefinition aspect.defaultValue="jdbc" baseType="STRING" description="jDBCConnectionURL" name="jDBCConnectionURL" ordinal="0"/>                             <FieldDefinition aspect.defaultValue="SELECT NOW()" baseType="STRING" description="Connection validation string" name="connectionValidationString" ordinal="0"/>                             <FieldDefinition aspect.defaultValue="org.postgresql.Driver" baseType="STRING" description="jDBCDriverClass" name="jDBCDriverClass" ordinal="0"/>                             <FieldDefinition baseType="STRING" description="Database user name" name="userName" ordinal="0"/>                             <FieldDefinition baseType="PASSWORD" description="Database password" name="password" ordinal="0"/>                         </FieldDefinitions>                     </DataShape>                     <Rows>                         <Row>                             <jDBCConnectionURL><![CDATA[jdbc:postgresql://localhost:5432/demo]]></jDBCConnectionURL>                             <maxConnections>100.0</maxConnections>                             <connectionValidationString><![CDATA[SELECT NOW()]]></connectionValidationString>                             <jDBCDriverClass><![CDATA[org.postgresql.Driver]]></jDBCDriverClass>                             <userName />                             <password />                         </Row>                     </Rows>                 </ConfigurationTable>                 <ConfigurationTable description="" isMultiRow="false" name="ConnectionMonitoring">                     <DataShape>                         <FieldDefinitions>                             <FieldDefinition aspect.defaultValue="1.0" baseType="NUMBER" description="Number of retries" name="numberOfRetries" ordinal="0"/>                             <FieldDefinition aspect.defaultValue="2000.0" baseType="NUMBER" description="Retry delay in milliseconds" name="retryDelay" ordinal="0"/>                             <FieldDefinition aspect.defaultValue="false" baseType="BOOLEAN" description="Enable connection monitoring" name="enableMonitor" ordinal="0"/>                             <FieldDefinition aspect.defaultValue="30000.0" baseType="NUMBER" description="Monitor rate in milliseconds" name="connectionMonitorRate" ordinal="0"/>                         </FieldDefinitions>                     </DataShape>                     <Rows>                         <Row>                             <numberOfRetries>1.0</numberOfRetries>                             <retryDelay>2000.0</retryDelay>                             <enableMonitor>false</enableMonitor>                             <connectionMonitorRate>3000.0</connectionMonitorRate>                         </Row>                     </Rows>                 </ConfigurationTable>             </ConfigurationTables>             <avatar/>             <DesignTimePermissions>                 <Create/>                 <Read/>                 <Update/>                 <Delete/>                 <Metadata/>             </DesignTimePermissions>             <RunTimePermissions/>             <InstanceDesignTimePermissions>                 <Create/>                 <Read/>                 <Update/>                 <Delete/>                 <Metadata/>             </InstanceDesignTimePermissions>             <InstanceRunTimePermissions/>         </ThingTemplate>     </ThingTemplates> </Entities>
View full tip
Neural Net is a learning algorithm that is inspired by how the human brain works. For example, imagine you love chocolate cake so much that, you joyfully exercise a bit more during the week just to enjoy that delicious chocolate cake without feeling guilty. But if the weather is terrible there is no way you go exercise, and then you can’t eat the delicious chocolate cake. Although, if your beautiful girlfriend / boyfriend exercise with you, then you ignore the weather and joyfully exercise and then you can enjoy that delicious chocolate cake without feeling guilty. The brain’s nervous system passes information using a synapse structure which allows neurons to pass information to other neurons and finally make a decision. This structure of passing information and decision making is the construction behind neural net algorithm. The data structure provides weights on the edges for the nodes/synapses in a directed graph. For example, our chocolate cake decision making could be translated into: w1=6 Whether or not your girlfriend or boyfriend exercise together with you w2=3 Enjoying delicious chocolate cake w3=2 Weather The high weights for example indicates the condition to have a high influence on your output decision making, while lower weight is not that influential. The illustration below is an example of neural net with 5 different input of information: The edges/arrows represent weights each input/node have a weight associated with it. These weights are applied when training neural net. Three of the inputs could represent 1= Delicious chocolate cake, 2= The weather, 3= Your girlfriend or boyfriend, and the last two inputs could be other information. The output is the condition of the decision determined by the hidden layer. ThingWorx Analytics Server applies neural net with full interconnection layer, which means each value from the input layer is duplicated and sent to each node in the hidden layer, just like in following illustration.
View full tip
Retraining the Model in ThingWorx Analytics When using ThingWorx Analytics Products to build Prediction Models, it is not enough to end up with models that are a Technical Success. The purpose is to ultimately have models that are a Business Success. What the user would want to achieve is to have Models that remain reliable and accurate in a potentially changing production environment. Therefore, when your environment changes, the model that you have used and relied on might no longer provide the same quality of results. Hence the need to retrain your model. Types of Models to be retrained: There are currently two types of models that are created with ThingWorx Analytics: Predictive models Anomaly Detection models Each of those models could require retraining based on the context in which they are created then used. When to retrain your Model: - Predictive models: For predictive analytics models, the main initiator for retraining would be a change in the production environment. resulting in the change of collected Dataset. This could nonetheless be caused by many factors: An overall change in the business objective: This could include a change in the granularity at which the Dataset is used. An Example, in a Company HR Dataset, could be moving from making predictions on a Department Level to making predictions on an Employee Level. The addition of either new features in the Dataset or even new values in the existing features which did not figure within the values of the training dataset. This type of change in the Dataset would require the retraining of the Model. The emergence of new trends in the marketplace: These new trends would appear in the generated Datasets. This could be detected by the degradation of the results that are provided by the existing Prediction Models. - Anomaly Detection models: In anomaly detection, the need to retrain the models originates mainly from a change in what is considered to be a Normal behavior of a certain monitored property. The could be caused by the following factors: A change in the context in which the Property values are measured then monitored: An example is monitoring the Traffic in a Street in the Working weekdays while excluding the weekends then adding the Weekend days to the monitored behavior. Here the change in the Traffic is normal however would be detected as an Anomaly unless the model is retrained. A change in the Thresholds of values accepted to be normal in a certain property. An example is the temperatures measured on a running device. The Device, previously,  never run at full power when the model was built but since it started running at full power the temperature increased beyond the usual threshold and thus the model needs to be retrained to include the new Normal temperatures. Another reason that could justify retraining the anomaly detection model is simply that when the model was trained the Property values that were used were not representing its normal state. For example, the temperature of an  Engine was being measured on a "Turned off" state when we are actually trying to build a model that would detect temperature anomalies on a running Engine. This might not be an exhaustive list of the reasons that would require either a Predictive or an Anomaly Detection Model to be retrained. As a general rule of thumb, if the model starts delivering results that are below expected or if the business context for the model is not valid, then it might be a wise decision to retrain the Analytics Model.
View full tip
Behavior of ThingWorx Analytics Anomaly Detection with Data Gaps In ThingWorx Analytics, Anomaly detection is performed through the ThingWatcher API framework. This is done by observing the Data from an Edge device, learning what the data stream should look like and then monitoring for any unexpected sequences of Data within the incoming Data stream. Ideally, for this process to work properly, there should be no Data Gaps. However, Data Gaps do occur, this blog describes how ThingWatcher deals with them in order to achieve high performance in anomaly detection. Data Gaps and phases affected: In anomaly detection, ThingWatcher goes through three consecutive phases which are Initializing, Calibrating and then Monitoring. Both the Initializing and Monitoring phases involve either collecting or monitoring streamed Data, so these two phases are sensitive to Data streaming Gaps. The Calibrating phase involves the use of already collected data to create the Anomaly Detection Model. Thus this phase is not directly affected by Data gaps. Dealing with Long and Short Data Gaps: Initializing Phase: During this phase, Data is collected and as part of the collection process, the sampling rate is imputed. So when short data gaps occur these are interpolated so that there are no missing values. However long Gaps might also occur. A Data gap is considered to be long if there is more than three missing Data points which should amount to three times the sampling rate. Basically, if the Timestamp on a data point is greater than the previous timestamp by more than three times the sampling rate that is considered to be a long gap. If a long gap occurs, ThingWatcher would restart the Data Collection process since long Data gaps are not acceptable. The data recollection process could be initiated three times when there are long gaps before failing if the gaps persist. The Data source would then no longer be considered reliable Monitoring Phase: In this phase, the Data stream is monitored to detect any unexpected behavior. In that case, if a short time gap occurs between the previous and the current TimedValue data points, the lookback buffer would be cleared. ThingWatcher will re-enter the Buffering state and will remain in this state until the lookback window buffer is completely filled. For more information on The functionalities of ThingWatcher, Please refer to the ThingWatcher Deployment Guide https://support.ptc.com/WCMS/files/173109/en/ThingWatcher-Deployment-Guide-8.0.pdf However, if the gaps are long and exceed three times the sampling rate, data filling could no longer be a valid solution and Data collection restarts. It is important to note that these imputed values decrease the accuracy of the Anomaly Detection Model. Therefore data monitored by ThingWatcher should be incremented in regular intervals. In general, persistent data gaps should be avoided by ensuring that data is streamed such that the timestamps increase in regular increments and any gaps that exist are generally incidental and small.
View full tip
In this video we show: - how to deploy the microservices via jar files - how to setup ThingWorx to use those microservices for anomaly detection   Updated Link for access to this video:  ThingWorx Analytics: Deploying Training and Result Microservices via jar files for Anomaly Detection
View full tip
This video is the 1 st part of a series of 3 videos walking you through how to setup ThingWatcher for Anomaly Detection. In this first video you will learn the basics of how to create connectivity between KEPServer and ThingWorx Platform.   Updated Link for access to this video:  Anomaly Detection 8.0 - Part 1: Connecting KEPServer to ThingWorx: Part 1 of 3
View full tip
In this video we go through the steps to install ThingWorx Analytics Server 8.0   Updated Link for access to this video:  ThingWorx Analytics Server 8.0
View full tip
Please refer to the release notes: PTC Here are some common questions and answers in regards to the Uprade change: Extension: The removal of dependencies was almost impossible. Do the changes allow extension updates without removal? That is correct Is uninstalling extensions "easier" possible? For example, having extensions with many dependencies which results in a struggle when manually deleting all include files...it would be great to have just 1 uninstall function. Unfortunately, that is still the case when one is uninstalling extension Is there an easy way to see all entity dependencies on an extension (vs. on any one template etc)? Not currently at the extension level.  That's certainly something to be considered adding If one made a mistake on changing an extension mashup, how do  they recover? Is it necessary to remove and then import newer extension? Yes, at the moment that is the only recourse.
View full tip
Please refer to the release notes to find information on the new features/changes: PTC Here are some common questions and answers in regards to the Installers feature: Does that mean Thingworx 8 only support docker installation? Or standalone installation is still allowed? Only if using the new installer.  The war file download will still be available for non Docker installs. The WAR files will still be available and usable  the same way as in the past.  Users only need to use Docker if they use the installer. How do customers download/build the docker image? The image is not provided separetly, it is installed and configured by the installer. Does the installer install docker when necessary as well? Or is it expected that the user already has docker installed? No, the user must install it on their operating system before using the installer.  The installer will detect if Docker is properly installed.
View full tip
This article https://support.ptc.com/appserver/cs/view/solution.jsp?n=CS264270 and the blog post provide information on the technical changes in Thingworx 8.0, New Technical Changes in ThingWorx 8.0.0 Here are some common questions and answers in regards to the change. Is there any way that one could lose access to the Appkey keystore? (restore a backup, etc) Yes, it is possible to  lose the key store. If one for some reason simply deletes it or renames it, the existing application keys would no longer be able to be decrypted - thus unusable. Is there a way to back up the appkey store and any necessary secrets such that it can be recovered then? Yes, but this would be handled at the file system, not inside of ThingWorx. We do have some best practices documented for managing the keystore. For developers who configure Edge applications and Connectors to connect to the platform, where do they copy the app key from? Are they copyring the encrypted version of the app key? They would do the same as before. We are not encrypting the app key in the UI, only on disk. Is there any way to get a summary of appkeys that are being used via URL? We do have some requirements for highlighting "repeat offenders". In 8.0, one can query the logs to find those app keys. We don't log the app key id, only the app key name.
View full tip
In this video we show a simple mashup and services in order to display the ThingPredictor's real time scoring results. This video applies to ThingWorx Analytics 7.4 to 8.1   Updated Link for access to this video:  Showing ThingWorx Analytics Manager's results (ThingPredictor) in a Mashup
View full tip
In this blog I will be testing with the WindchillSwaggerConnector, but most of the steps also apply to the generic SwaggerConnector.     Overview   The WindchillSwaggerConnector enables the connection to the Windchill REST endpoints through the Swagger (OpenAPI) specification. It is a specialized implementation of the SwaggerConnector. See Integration Connectors for documentation.   It relies on three components : Integration Runtime : microservice that runs outside of ThingWorx and has to be deployed separately, it uses Web Socket to communicate with the ThingWorx platform (similar to EMS). Integration Subsystem : available by default in 7.4 (not extension needed) Integration Connectors (WindchillSwaggerConnector) : available by default in 7.4 (not extension needed)   Currently, in 7.4, the WindchillSwaggerConnector  does not support SSO with Windchill (it is more targeted for a "gateway type" integration). Note that the PTC Navigate PDM apps are using the WindchillConnector and not the WindchillSwaggerConnector.   Integration Runtime microservice setup   The ThingWorx Integration Runtime is a microservice that runs outside of ThingWorx. It can run on the ThingWorx server or a remote machine. It is available for download from the ThingWorx Marketplace (Windows or Linux). The installation media contains 2 files : 1 JAR and 1 JSON configuration file.   For this demo, I'm installing the Integration Runtime on a remote machine and will not be using SSL.   1. Prerequisite for the Integration Runtime : Oracle Jre 8 (and of course a ThingWorx 7.4 platform server accessible) 2. Create an ApplicationKey in the composer for the Integration Runtime to use for communication to the ThingWorx platform. 3. Configure the Integration Runtime communication - ThingWorx host, port, appKey, ... - this is done on the Integration Runtime server via the JSON configuration file.   My integrationRuntime-settings.json (sslEnable=false, storagePath is ignored) : { "traceRoutes": "true", "storagePath": "/ThingworxStorage", "Thingworx": {     "appKey": "1234abcd-xxxx-yyyy-zzzz-5678efgh",     "host": "twx74neo",     "port": "8080",     "basePath": "/Thingworx",     "sslEnable": "false",     "ignoreSSLErrors": "true"   } } Note : It is important to completely remove the "SSL": {} block when not using SSL   4. Launch the Integration Runtime service (update the JAR and JSON filenames if needed) java -DconfigFile=integrationRuntime-settings.json -jar integration-runtime-7.4.0-b12.jar The Integration Runtime service uses Web Socket to communicate with the ThingWorx platform (similar to EMS). It registers itself with the ThingWorx platform.   Monitoring the Integration Runtime microservice        In the ThingWorx composer : Monitoring > Subsystems > Integration Subsystem      SMAINENTE1D1 is the hostname of my Integration Runtime server.   Custom WindchillSwaggerConnector implementation   Use the New Composer UI (some setting, such as API maps, are not available in the ThingWorx legacy composer) 1. Create a DataShape that is used to map the attributes being retrieved from Windchill WNCObjectDS : oid, type, name (all fields of type STRING)   2. Create a Thing named WNC11Connector that uses WindchillSwaggerConnector as Thing Template 3. Setup the Windchill connection under WNC11Connector > Configuration Authentication Type = fixed (SSO currently not supported) Username = <Windchill valid user> Password = <password for the Windchill user> Base URL : <Windchill app URL> (e.g. http://wncserver/Windchill)   4. Create an API maps under WNC11Connector > Services and API Maps > API Maps (New Composer only) My API Map : New API Map Mapping ID : FindBasicObjectsMap EndPoint : findObjects (choose the first one) Select DataShape : WNCObjectDS (created at step 1) and map the following attributes : name <- objName ($.items.attributes) type <- typeId ($.items) oid <- id ($.items)   After pressing [done] verify that the API ID is '/objects GET' (and not /structure/objects - otherwise recreate the mapping and choose the other findObjects endpoint). 5. Create a "Route" service under WNC11Connector > Services and API Maps > Services (New Composer only) Name : FindBasicObjects Type (Next to [Done] button) : Route Route Info | Endpoint : findObjects (same as step 4) Route Info | Mapping ID : FindBasicObjectsMap-xxxx created at step 4 Testing our custom WindchillSwaggerConnector   Test the WNC11Connector::FindBasicObjects service Note that the id (oid) and typeId (type) are returned by default by the /objects REST API - objName has to be explicitly requested.   Monitoring the Integration Connector        In the ThingWorx composer : Monitoring > Integration Connectors
View full tip
One recurring question that comes up after a customer has been using the scripting capabilities of the Axeda Platform for some time is how to create function libraries that are reusable, in order to reduce the amount of copy and pasting (and testing) that is done to create new functionality.  Below I demonstrate a mechanism for how to accomplish this.  Some things that are typically included in such a library are: Customized ExtendedObject access methods (CRUD) - ExtendedObjects must be created before they can be used, so this can be encapsulated per customer requirements DataItem manipulation Gathering lists of Assets based on criteria Accessing the ExternalCredentialsBridge For those unfamiliar with Custom Objects I suggest some resources at the end of this document to get started.  The first thing we want to do is create a script that is going to be our "Library of Functions": FunctionLibrary.groovy: class GroovyChild {     String hello() {         return "Hello"     }     String world() {         return "World"     } } return new GroovyChild() This can the be subsequently called like this: FunctionCaller.groovy: import static com.axeda.sdk.v2.dsl.Bridges.* import com.axeda.services.v2.CustomObjectCriteria import com.axeda.services.v2.CustomObjectType import com.axeda.services.v2.CustomObject CustomObjectCriteria cOC1 = new CustomObjectCriteria() cOC1.setName 'FunctionLibrary' def co = customObjectBridge.findOne cOC1 result = customObjectBridge.execute(co.label ) return ['Content-Type': 'application/text', 'Content': result.hello() + ' ' + result.world() ] A developer would be wise to add in null checking on some of the function returns and do some error reporting if it cannot find and execute the FunctionLibrary.  This is only means a a means to start the users on the path of building their own reusable content libraries. Regard -Chris Kaminski PTC/Axeda Customer Support References: Axeda® Platform Web Services Developer's Reference, v2 REST 6.8.3 August 2015 Axeda® v2 API/Services Developer's Reference Version 6.8.3 August 2015 Axeda® v1 API Developer’s Reference Guide Version 6.8 August 2014 Documentation Map for Axeda® 6.8.2 January 2015
View full tip
Announcements