IoT Tips

  Question: What should I know about using ThingWorx with InfluxDB to store my time series data? Hi, ThingWorx users!   It’s here! Thanks for waiting patiently since my previous post announcing ThingWorx’ new support of InfluxDB as a time series persistence provider.   As of our 8.4 release, you can now use InfluxDB to store your ThingWorx time series data with incredible power and ease.   Want to learn more? Check out the following FAQs:   1. What is InfluxDB? Who is InfluxData? InfluxDB is a time series database designed to handle high write and query loads. It is meant to be used as a backing store for any use case involving large amounts of timestamped data, like monitoring, application metrics, IoT sensor data, and real-time analytics that you’d find in ThingWorx.   InfluxDB is created by InfluxData, an awesome company that we are proud to call a PTC partner.   2. When would I want to use InfluxDB for IIoT? While the ThingWorx IIoT platform supports multiple databases to persist IIoT data and is agnostic when it comes to the storage layer, InfluxDB is the ideal choice for time series. When the number of connected devices increases, along with the amount of streaming data, the need to have a high-scale telemetry database choice is obvious.   For very high scale data ingestion, InfluxDB should be used as a persistent provider with the ThingWorx platform for multiple reasons. Its flexibility and ease of use provides native support for standard time series functions, including: sampling, interpolation, time bucketing, aggregation, selector, transformation, predictor, etc. It does all of this while supporting a high compression of data (~45x) with the ability to handle thousands of writes per second and read thousands of rows in milliseconds.   Check out this article by our Enterprise Deployment Center (EDC) explaining why InfluxDB is great for small ThingWorx applications.   3. What are the three different flavors of InfluxDB? InfluxDB Open Source (TICK Stack), InfluxDB Enterprise & InfluxDB Cloud. Here’s more info on each: InfluxDB Open Source (TICK Stack): This is the open-source version of the product available to download via the InfluxData website. Also included here are the other projects that comprise the TICK Stack, including: [T] Telegraf; open source collection agent [I] InfluxDB; open source time series database [C] Chronograf; open source visualization application [K] Kapacitor; open source streaming processing engine; side car to InfluxDB InfluxDB Enterprise: This is the commercial software version of InfluxDB for high availability clustering and the recommended time series database to be used for production with ThingWorx 8.4 and later. InfluxDB Enterprise works with the rest of the TICK stack interchangeably (Telegraf, Chronograf, Kapacitor). InfluxDB Cloud: This is the commercial service version of InfluxDB, hosted on AWS, managed by InfluxData, and delivered as a service to customers. InfluxDB Cloud works with the rest of the TICK stack interchangeably (Telegraf, Chronograf, Kapacitor). To learn more about the different modules of InfluxDB (Telegraf, Chronograf, Kapcitor), check out InfluxData Introduction for documentation or InfluxData Products for product info.   4. What is the difference between InfluxDB opensource and enterprise? InfluxDB Open Source is available in a single (1 only) data node configuration only, albeit with “n” number of vCPU or “cores” provisioned on that single node.  InfluxDB Enterprise is available in multiple (2 or more) data node configuration, also with “n” number of vCPU or “cores” provisioned to each node. The Enterprise edition is generally preferred for production deployments that require high availability, replication, and redundancy. Provisioned along with the data nodes are three (3) meta nodes and a load balancer to distribute data workload across the multiple nodes. Typical configurations are in even increments of data nodes (i.e. 2, 4, 6, 8, etc.).   5. Where can I find the pricing overview for buying enterprise licenses for InfluxDB? The PTC product and go-to-market team have defined commercial pricing for InfluxDB Enterprise. For help with pricing, reach out to Chris Wensley (cwensley@ptc.com) and Anders Hinrichsen (anders@influxdata.com).   6. How do I configure InfluxDB with ThingWorx? We’ve outlined the steps for you in the ThingWorx Help Center and created a quick video to instruct you on how to install InfluxDB with ThingWorx. (view in My Videos) To see the current version of InfluxDB that we support, read our ThingWorx 9.0 System Requirements guide.   7. How do I configure InfluxDB and ThingWorx in a high availability scenario? With the ability to leverage multiple data stores, we work to provide the flexibility to best meet the needs of your IT preferences and investments. InfluxDB helps us do that. To configure ThingWorx for High Availability, please refer to this section of the ThingWorx Platform 9 Help Center. To configure InfluxDB for High Availability at the database level, please refer to InfluxData’s documentation on how to Install and deploy InfluxDB Enterprise clusters.   8. Where can I learn more about how to monitor and manage InfluxDB? Monitoring info for InfluxDB can be found here: Monitoring Tools for TICK Stack.   9. How can I tune and optimize InfluxDB with ThingWorx? The best approach for running InfluxDB with PTC ThingWorx 8.4 (or later) is to treat the workload and configuration just as you would in a stand-alone deployment. We suggest to stick to the recommendations in the InfluxDB and TICK stack documentation.   10. How do I perform backup and recovery of ThingWorx with InfluxDB? Please see the ThingWorx Platform Backup and Recovery Planning Technical Brief to plan for back and recovery. You can also find more more details on taking backups and restoring data from InfluxDB in the Backing up and restoring in InfluxDB Enterprise overview.   11. Where can I learn more about sampling, interpolation, time bucketing, aggregation, pivot​ and other key features of InfluxDB? Features of InfluxDB can be found here: InfluxData Time Series Platform. Implementation of InfluxDB features can be found here: Getting Started with InfluxDB.   12. What are all the different persistence providers supported with ThingWorx? When should I use InfluxDB? ThingWorx supports the following model and data provider storage options: H2, PostgreSQL, MS SQL Server and AzureSQL ThingWorx supports the following data provider only storage options: InfluxDB Please refer to the model and data best practices section of the ThingWorx 9 Help Center for further information on options how to store your model and data with ThingWorx.   We have also updated the ThingWorx Platform 9.0 Sizing Guide to provide relevant information to estimate the amount of processing and memory that ThingWorx may need to meet your requirements. It also provides guidance on when to use InfluxDB for your scale needs.   13. When should I use InfluxDB over DataStax Enterprise (DSE)? Here is a good blog post that benchmarks time series data performance of InfluxDB vs. Cassandra, which is the core of DataStax Enterprise (DSE). In specific use cases, InfluxData Enterprise may be more cost effective when compared to similar telemetry use cases with DSE.   14. How can I migrate my data from PostgreSQL to InfluxDB? Migration from PostgreSQL or MSSQL is supported by the ThingWorx in-built data tools, which can export entities and data from PostgreSQL or MSSQL and then import them into InfluxDB.   Details on how to upgrade to ThingWorx 9.0 can be found in the Upgrading ThingWorx  section of the ThingWorx 9 Help Center.   15. Should I use InfluxDB as a time series store rather than OSI PI, IP21, or others? For ThingWorx 8.4 and later, InfluxDB is the recommended time series store. This can be implemented at the edge with ThingWorx (i.e. “front end”) using the open source edition and can also be implemented at the hub (i.e. “back end”) using either of the commercial editions designed for HA production workloads.   As always, ThingWorx can connect to most industrial software, including OSI PI, IP21, etc. with our integration toolset.   That’s a wrap—almost! We’ve added two extra questions for you.   16. What’s on the roadmap for ThingWorx with InfluxDB? Key development work to fully leverage built-in InfluxDB querying capabilities and support InfluxDB 2.0 in future ThingWorx releases Leveraging query operations capabilities from InfluxDB to further improve query performance Supporting additional native InfluxDB features (e.g. continuous queries)   17. What should I do if I need technical support with InfluxDB? If you select InfluxDB as your persistence provider, then all support requests related to configuring InfluxDB can be logged through PTC Technical Support at https://support.ptc.com or by calling 1-800-477-6435. You may also want to use the PTC Community to learn and collaborate with the growing PTC developer community. For all other requests related to database management, troubleshooting, monitoring, and administration, we encourage you to reach out to InfluxData directly based on your enterprise purchase contract made with InfluxData. PTC customers using InfluxDB can also email ptc-support@influxdata.com for support requests related to InfluxData.   If you’re as excited as I am about the ability to store your time series data with InfluxDB, let me know in the comments below!   Until next time, if you have any questions, just ask Kaya!

In this blog I will be testing the SAPODataConnector using the SAP Gateway - Demo Consumption System.   Overview   The SAPODataConnector enables the connection to the SAP Netweaver Gateway through the ODdata specification. It is a specialized implementation of the ODataConnector. 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 since 7.4 (not extension needed) Integration Connector : SAPODataConnector available by default in 8.0 (not extension needed)   ThingWorx can use OAuth to access SAP, but in this blog I will just use basic authentication.   SAP Netweaver Gateway Demo system registration   1. Create an account on the Gateway Demo system (credentials to be used on the connector are sent by email) 2. Verify that the account has access to the basic OData sample service : https://sapes4.sapdevcenter.com/sap/opu/odata/IWBEP/GWSAMPLE_BASIC/   Integration Runtime microservice setup   1. Follow WindchillSwaggerConnector hands-on (7.4) - Integration Runtime microservice setup Note: Only one Integration Runtime instance is required for all your Integration Connectors (Multiple instances are supported for High Availability and scale).   SAPODataConnector setup   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 SAP SAPObjectDS : Id (STRING), Name (STRING), Price (NUMBER) 2. Create a Thing named TestSAPConnector that uses SAPODataConnector as thing template 3. Setup the SAP Netweaver Gateway connection under TestSAPConnector > Configuration Generic Connector Connection Settings Authentication Type = fixed HTTP Connector Connection Settings Username = <SAP Gateway user> Password = < SAP Gateway pwd> Base URL : https://sapes4.sapdevcenter.com/sap Relative URL : /opu/odata/IWBEP/GWSAMPLE_BASIC/ Connection URL : /opu/odata/IWBEP/GWSAMPLE_BASIC/$metadata 4. Create the API maps and service under TestSAPConnector > API Maps (New Composer only) Mapping ID : sap EndPoint : getProductSet Select DataShape : SAPObjectDS (created at step 1) and map the following attributes : Name <- Name Id <- ProductID Price <- Price Pick "Create a Service from this mapping"     Testing our Connector   Test the TestSAPConnector::getProductSet service (keep all the input parameters blank)

Overview The Axeda Platform is a secure and scalable foundation to build and deploy enterprise-grade applications for connected products, both wired and wireless. This article provides you with a detailed feature overview and helpful links to more in-depth articles and tutorials for a deeper dive into key areas. Types of Connected Product Applications M2M applications can span many vertical markets and industries. Virtually every aspect of business and personal life is being transformed by ubiquitous connectivity. Some examples of M2M applications include: Vehicle Telematics and Fleet Management - Monitor and track the location, movements, status, and behavior of a vehicle or fleet of vehicles Home Energy Monitoring - Smart energy sensors and plugs providing homeowners with remote control and cost-saving suggestions Smart Television and Entertainment Delivery - Integrated set-top box providing in-view interaction with other devices – review your voicemails while watching a movie, chat with your Facebook friends, etc. Family Location Awareness - Set geofences on teenagers, apply curfews, locate family members in real time, with vehicle speed and condition Supply Chain Optimization - Combine status at key inspection points with logistics and present distribution managers with an interactive, real-time control panel Telemedicine - Self-monitoring/testing, telecardiology, teleradiology Why Use a Platform? Have you ever built a Web application? If so, you probably didn't create the Web server as part of your project. Web servers or Web application servers like Apache HTTPd or JBoss manage TCP sockets, threads, the HTTP protocol, the compilation of scripts, and include thousands of other base services. By leveraging the work done by the dedicated individuals who produced those core services, you were able to rapidly build an application that provided value to your users.  The Axeda Platform is analogous to a Web server or a Web application server, but provides services and a design paradigm that makes connected product development streamlined and efficient. Anatomy of an Axeda Connected Product Connected Products can really be anything that your product and imagination can offer, but it is helpful to take pause for some common considerations that apply to most, if not all of these types of solutions. Getting Connected - Bring your product or equipment to the network so that it can provide information to the solution, and react to commands and configuration changes orchestrated by the application logic. Manage and Orchestrate - Script your business logic in the cloud to tie together remote information with information from other business systems or cloud-based services, react to real-time conditions, and facilitate batch operations to synchronize, analyze, and integrate. Present and Report - Build your user experiences, enabling people to interact with your connected product, manage workflows around business processes, or facilitate data analysis. Let's take a look at the Axeda Platform services that help with each of these solution considerations. Getting Connected Wired & Wireless Getting connected can assume all sorts of shapes, depending on the environment of your product and the economics of your solution. The Axeda Platform makes no assumption about connectivity, but instead provides features and functionality to help you connect.For wireless applications, especially those which may use cellular or satellite communications, the speed and cost of communication will be an important factor.  For this reason, Axeda has created the Adaptive Machine Messaging Protocol (AMMP), a cross-platform, efficient HTTP-based protocol that you can use to communicate bi-directionally with the platform.  The protocol is expressive, robust, secure, and most importantly, able to be implemented on a wide range of hardware and programming environments. When you are faced with connecting legacy products that may be communicating with a proprietary messaging protocol, the Axeda Platform can be extended with Codecs to "learn" your protocol by translating your device's communication format into a form that the platform can understand. This is a great option for retrofitting existing, deployed products to get connectivity and value today, while designing your next-generation of product with AMMP support built-in. Manage and Orchestrate The Data Model defines the information and its behavior in the Axeda Platform. Rules Rules form the heart of a dynamic connected application. There are three types of rules that can be leveraged for your orchestration layer: Expression rules run in the cloud, and are configured and associated with your assets through the Axeda Admin UI or SDK. These rules have an If-Then-Else structure that's easy to create and understand. They're like a formula in a spreadsheet. For example, say your asset has a dataitem reading for temperature: [TODO: Image of dataitem TEMP] This rule compares the temperature to 80 every time a reading is received. When this happens, the rule creates an alarm with name "High Temp" and severity 100.Learn more about Expression Rules. State Machines help organize Expression Rules into manageable groups that apply to assets when the assets are in a certain state. For example, if your asset were a refrigerated truck, and you were interested in receiveing an alert when the temperature within the cargo area rose above a preset threshold, you would not want this rule to be applied when your truck asset is empty and parked in the distribution center lot. In this case, you might organize your rules into a state machine called “TruckStatus”. The TruckStatus state machine would then consist of a set of states that you define, and the rules that execute when the truck is in a particular state. [TODO - show state machine image?] State “Parked”: IF doorOpen THEN … State “In Transit”: IF temperature > 40 THEN… State “Maintenance”: <no rules> You can learn more about state machines in an upcoming technical article soon. Scripting Using Axeda Custom Objects, you can harness the power of the Axeda SDK to gain access to the complete set of platform data and functionality, all within a script that you customize. Custom Object scripts can be invoked in an Expression Rule to provide customized and flexible business logic for your application. Custom Object scripts are written in a powerful scripting language called Groovy, which is 100% Java syntax compatible. Groovy also offers very modern, concise syntax options that make your code simple and easy to understand. Groovy can also implement the body of a web service method. We call this Scripto. Scripto allows you to write code and call that code by name through a REST web service. This allows a client application to call a set of customized web services that return exactly the information and format needed by the application. Here is a beginning tutorial on Scripto.  This site includes many Scripto examples called by different Rich Internet Applications (RIA). Location-Based Services Knowing where something is, and where it has been, opens a world of possible application features. The Axeda Platform can keep track of an asset’s current and historical location, which allows your applications to plot the current position of your assets on a map, or show a breadcrumb trail of where a particular asset has been. Geofences are virtual perimeters around geographic locations. You can define a geofence around a physical location by describing a center point and radius, or by “drawing” a polygon around an arbitrary shape. For instance, you may have a geofence around the Boston metro that is defined as the center of town with a 10-mile radius. You may then compare an asset’s location to this geofence in a rule and trigger events to occur. IF InNamedGeofence(“Boston”) THEN CreateAlarm(…) You can learn more about geofences and other location-oriented rule features in an upcoming tutorial. Integration Queue In today’s software landscape, almost no complete solution is an island unto itself. Business systems need to interoperate by sharing data and events, so that specialized systems can do their job without tight coupling. Messaging is a robust and capable pattern for bridging the gap between systems, especially those that are hosted in the cloud. The Axeda Platform provides a message queue that can be subscribed to by external systems to trigger processes and workflows in those systems, based on events that are being tracked within the platform. A simple ExpressionRule can react to a condition by placing a message in the integration queue as follows: IF Alarm.severity > 100 THEN PublishObject() A message is then placed in the queue describing the platform event, and another business system may subscribe to these messages and react accordingly. Web Services Web Services are at the heart of a cloud-based API stack, and the Axeda Platform provides full comprehensiveness or flexibility. The platform exposes Web Service operations for all platform data and configuration meta data. As a result, you can determine the status of an asset, query historical data for assets, search for assets based on their current location, or even configure expression rules and other configuration settings all through a modern Web Service API, using standard SOAP and REST communication protocols. Scripto Web Service APIs simplify system integration in a loosely-coupled, secure way, and we have a commitment to offering a comprehensive collection of standard APIs into the Axeda Platform. But we can't have an API that suits every need exactly. You may want data in a particular format, such as CSV, JSON, or XML. Or some logic should be applied and its inefficient to query lots of data to look for the bit you want. Wouldn’t you rather make the service on the other side do exactly what you want, and give it to you in exactly the format you need? That is Scripto – the bridge between the power and efficiency of the Axeda Custom Object scripting engine, and a Web Service client. Using Scripto, you can code a script in the Groovy language, using the Axeda SDK and potentially mashing up results from other systems, all within the platform, and expose your script to an external consumer via a single, REST-based Web Service operation. You create your own set of Web Services that do exactly what you want. This powerful combination let’s you simplify your Web Service client code, and give you easy access and maintainability to the scripted logic. Present Rich Internet Applications are a great way to build engaging, information-rich user experiences. By exposing platform data and functions via Web Services and Scripto, you can use your tool of choice for developing your front-end. In fact, if you choose a technology that doesn’t require a server-side rendering engine, such as HTML + AJAX, Adobe Flash, or Microsoft Silverlight, then you can upload your application UI files to the Axeda Platform and let the platform serve your URL! Addition references for using RIAs on the Axeda Platform: Axeda Sample Application: Populating A Web Page with Data Items Extending the Axeda Platform UI - Custom Tabs and Modules Far-Front-Ends and Other Systems If a client-only user RIA interface is not an option for you, you can still use Web Services to integrate platform information into other server-side presentation technologies, such as Microsoft Sharepoint portal or a Java EE Web Application. You can also get lightning-fast updates for your users with the Axeda Machine Streams that uses ActiveMQ JMS technology to stream data in real-time from the Axeda Platform to your custom data warehouses.  While your users are viewing a drill down on a set of assets, they can receive asynchronous notifications about real-time data changes, without the need to constantly poll Web Services. Summary Building Connected Products and applications for them is incredibly rewarding job. Axeda provides a set of core services and a framework for bringing your application to market.

Everywhere in the Thingworx Platform (even the edge and extensions) you see the data structure called InfoTables.  What are they?  They are used to return data from services, map values in mashup and move information around the platform.  What they are is very simple, how they are setup and used is also simple but there are a lot of ways to manipulate them.  Simply put InfoTables are JSON data, that is all.  However they use a standard structure that the platform can recognize and use. There are two peices to an InfoTable, the DataShape definition and the rows array.  The DataShape is the definition of each row value in the rows array.  This is not accessible directly in service code but there are function and structures to manipulate it in services if needed. Example InfoTable Definitions and Values: { dataShape: {     fieldDefinitions : {           name: "ColOneName", baseType: "STRING"     },     {           name: "ColTwoName", baseType: "NUMBER"     }, rows: [     {ColOneName: "FirstValue", ColTwoName: 13},     {ColOneName: "SecondValue, ColTwoName: 14}     ] } So you can see that the dataShape value is made up of a group of JSON objects that are under the fieldDefinitions element.  Each field is a "name" element, which of course defined the field name, and the "baseType" element which is the Thingworx primitive type of the named field.  Typically this structure is automatically created by using a DataShape object that is defined in the platform.  This is also the reason DataShapes need to be defined, so that fields can be defined not only for InfoTables, but also for DataTables and Streams.  This is how Mashups know what the structure of the data is when creating bindings to widgets and other parts of the platform can display data in a structured format. The other part is the "rows" element which contains an array of of JSON objects which contain the actual data in the InfoTable. Accessing the values in the rows is as simple as using standard JavaScript syntax for JSON.  To access the number in the first row of the InfoTable referenced above (if the name of the InfoTable variable is "MyInfoTable") is done using MyInfoTable.rows[0].ColTowName.  This would return a value of 13.  As you can not the JSON array index starts at zero. Looping through an InfoTable in service script is also very simple.  You can use the index in a standard "for loop" structure, but a little cleaner way is to use a "for each loop" like this... for each (row in MyInfoTable.rows) {     var colOneVal = row.ColOneName;     ... } It is important to note that outputs of many base services in the platform have an output of the InfoTable type and that most of these have system defined datashapes built into the platform (such as QueryDataTableEntries, GetImplimentingThings, QueryNumberPropertyHistory and many, many more).  Also all service results from query services accessing external databases are returned in the structure of an InfoTable. Manipulating an InfoTable in script is easy using various functions built into the platform.  Many of these can be found in the "Snippets" tab of the service editor in Composer in both the InfoTableFunctions Resource and InfoTable Code Snippets. Some of my favorites and most commonly used... Create a blank InfoTable: var params = {   infoTableName: "MyTable" }; var MyInfoTable= Resources["InfoTableFunctions"].CreateInfoTable(params); Add a new field to any InfoTable: MyInfoTable.AddField({name: "ColNameThree", baseType: "BOOLEAN"}); Delete a field: MyInfoTable.RemoveField("ColNameThree"); Add a data row: MyInfoTable.AddRow({ColOneName: "NewRowValue", ColTwoName: 15}); Delete one or more data row matching the values defined (Note you can define multiple field in this statement): //delete all rows that have a value of 13 in ColNameOne MyInfoTable.Delete({ColNameOne: 13}); Create an InfoTable using a predefined DataShape: var params = {   infoTableName: "MyInfoTable",   dataShapeName: "dataShapeName" }; var MyInfoTable = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape(params); There are many more functions built into the platform, including ones to filter, sort and query rows.  These can be extremely useful when tying to return limited or more strictly structured InfoTable data.  Hopefully this gives you a better understanding and use of this critical part of the Thingworx Platform.

Installing an Open Source Time Series Platform For testing InfluxDB and its graphical user interface, Chronograf I'm using Docker images for easy deployment. For this post I assume you have worked with Docker before.   In this setup, InfluxDB and Chronograf will share an internal docker network to exchange data.   InfluxDB can be accessed e.g. by ThingWorx via its exposed port 8086. Chronograf can be accessed to administrative purposes via its port 8888. The following commands can be used to create a InfluxDB environment.   Pull images   sudo docker pull influxdb:latest sudo docker pull chronograf:latest   Create a virtual network   sudo docker network create influxdb   Start the containers   sudo docker run -d --name=influxdb -p 8086:8086 --net=influxdb --restart=always influxdb sudo docker run -d --name=chronograf -p 8888:8888 --net=influxdb --restart=always chronograf --influxdb-url=http://influxdb:8086     InfluxDB should now be reachable and will also restart automatically when Docker (or the Operating System) are restarted.

Internationalization and Localization Internationalization (often abbreviated I18N – from "I" + 18 more letters + "n") is the process of developing software that supports many languages, including those with non-Latin character sets. Localization (L10N) refers to developing applications that can be delivered in many languages, relying on the underlying architecture of I18N. This how-to article focuses mostly on localization, since the infrastructure is in place and stable. Create a Localization Table You create a Localization Table entity when you need to add support for another language to the application you're developing. Someone from Sales has said "There's an opportunity if we can deliver the Spiffy application in Estonian." This suggests that an Estonian-speaking end user should be able to run Spiffy and see all of its labels, messages, prompts, dialogs, and so on in Estonian. Most of the cost of adding Estonian language support is in a (usually contracted) service that does the English-to-Estonian (or whatever target language) translations. Such services employ native speakers who can get the nuances of translation correct. See Tips for translators below for suggestions on improving the accuracy of the translation. In Composer, view the Localization Tables list. Begin by duplicating an existing table (e.g. check Default or another language and click Duplicate) or by clicking New. A new tab will open with a New Localization Table in edit mode. The fields shown are: Locale (required). This is the official language tag of the new language. Language tags are defined by an Internet standard, IETF BCP 47. Briefly, they consist of a standard abbreviation for a language (e.g. en for English, de for German), followed optionally by a script subtag (e.g. Cyrl for Cyrilic), followed optionally by a region code (a country code, such as CH for Switzerland or HK for Hong Kong, or a U.N. region number), followed optionally by other qualifiers such as dialect. A simple example is es, Spanish. A complex one is sl-Latn-IT-nedis, Slovenian rendered in Latin characters as spoken in Italy in the Natisone dialect. Software rarely needs such highly specific language tags; the most specific practical examples are the various scripts and regions for Chinese (e.g. zh-Hans-CN, zh-Hant-TW). Language Name (Native) (required). This is the name of the language as written in that language, such that it would be readable by a native speaker. For example, 日本語 for Japanese, ਪੰਜਾਬੀ ਦੇ for Punjabi, or Deutsche for German. Language Name (Common). This is the name of the language as written in a common administrative language. For an application delivered internationally, English is probably a safe choice. Administrators at a customer site might change these to be in the language of the headquarters country. Description. Free form text describing the language. This will appear to end-users as a tooltip as they hover over language choices. Tags. Standard ThingWorx entity tags. Home Mashup. Does not apply. Avatar. An icon for this language. The default is . No other icons are delivered as standard, but language selection interfaces in many products use national flags to help distinguish choices, and those could be supplied here. Avatars are 48x48px images. There may be political implications in choosing a flag or other symbol for a language; use caution. Note that subtags of a language tag are separated by a hyphen, as in zh-Hans-SG. Using underscore is a Java convention that does not conform to BCP 47.A complete properties definition for Czech might look like this: Once the table has been created and saved, you can edit the translated text in Composer. Under Entity Information, select Localization Tokens. A grid similar to this will appear: The columns shown are: Token Name. This is the symbol used by mashup developers to insert a localized string into a certain place in a widget. For example, no matter how the phrase "Add New Page" is rendered (Neue Seite hinzufügen, Adicionar nova página, 새 페이지 추가...) the application developer is only concerned that the token addNewPage appears on the proper widget. See How tokens are resolved below for more information. This Language. How the text is to be represented in this language, that is, the language of the Localization Table currently being viewed or edited. Language. How the text has already been represented in any other language currently defined on the system. This is simply for reference purposes, to compare one translation with another. Usage. Can be set to Label, Message, or left unspecified. This is a guide to translators, who have to be concerned about the size of translated text. Usage Label suggests that the text needs to fit in a confined space, such as in a column header or on the face of a button. Usage Message suggests that the text is meant for a popup, error message, help, or somewhere that full sentences can be accommodated. Context. This is a free-form text field to provide instructions, advice, context, or other explanatory material to the translator. For the token book, for example, the context field can distinguish between the senses of book (something to read), book a table, book a sale, or book a prisoner, which may all have different translations. Translations can be entered in Composer. However, it's also likely that a third-party translator will do the work without using this editor. See Tips for translators below. Define language preferences for a user The reason for localization is to present user interfaces in the best language for a given user. To support this, each ThingWorx user is associated with one or more languages – those that that user can read comfortably. Some applications might offer just one language or a few, some many, and the supported languages may or may not overlap. So each user defines an ordered preference list, saying in effect: my best language is Catalan, but I'm decent in Spanish, and if those aren't available I did spend a few years in Hungary, and as a last resort there was some French in school. This would be represented in ThingWorx as: ca,es,hu,fr. A user from Scotland might have language preference en-UK,en, meaning that English with United Kingdom spellings and vocabulary is best (tyre, windscreen), but if not available then any English will do (tire, windshield). (It is not necessary to spell out related preferences of this type – see How tokens are resolved.) Any application then interacts with a given user in the best language that the application and user have in common.To define the language preference(s) for a user, open the Users list in Composer: Then choose an existing user to edit, or click New to create a new account. The only localization related information here is the Languages field. An administrator who knows the names of available languages may edit or paste an ordered, comma-separated list into the Languages field (e.g.  ca,es,hu,fr-CA). Clicking the Edit... button brings up a drag-and-drop preferences editor: The column on the left shows available (unselected) languages. The column on the right shows this user's languages, with the top entry being the most preferred language. Dragging a language from left to right adds it to the user's list; from right to left removes it; dragging rows up and down on the right changes the preference order. As language entries are dragged, a highlight appears to show where they might be dropped: A user with no language preference set will have all tokens resolved from the Default and System tables. Language Preferences can be set programmatically, as detailed in KCS Article CS243270. Localize Mashups The job of the application developer is to keep hard-coded natural language strings out of applications. To support this, widgets define an attribute isLocalizable: true for widget properties that can contain text. This shows up in the Mashup editor as a globe icon next to each localizable property. In this example, both the Text and ToolTipField properties are localizable: Clicking the globe icon changes the property from static to localized. The appearance in the Mashup editor changes accordingly: Clicking the magic wand icon opens the localization token picker: The list of tokens on the right corresponds to the Token Name column in the Localization Table editor. This is the key that is common to the meaning of a word or phrase, independent of its translation into natural languages. Select one from the list, or click to create a new one. Enter the token name and its Default (usually English) value: Note that, complying with best practices for extension developers, the token name has been namespaced: this token belongs to Acme Inc.'s Spiffy application. The rest of the name is descriptive and may reflect other development standards.When a new token is created, it becomes available to edit in every configured Localization Table. If these are not updated, then the default (English) value will be shown wherever the token occurs. How tokens are resolved What happens at run time when the UI needs to display the value of a localization token? The answer is determined by the current user's language preferences the set of Localization Tables configured on the system the presence or absence of a translation for a given token in a given table To visualize this, picture the user's language preferences as a stack, with the most preferred language on top and the least one sitting on the floor – where the floor consists of the Default and System Localization Tables: The user's language preference is fr,pt,ru,hi (French, Portuguese, Russian, Hindi, with French most preferred). The system is configured with Localization Tables, which have no order, for it (Italian), fr-CA (Canadian French), ru (Russian), pt-BR(Brazilian Portuguese), es (Spanish), and the default (likely Engish). Now the UI needs to present this user with the best value for the token com.acme.spiffy.labelAssembly. To resolve this, we start at the top of the stack. Is there a fr Localization Table? There is. Does it contain a translation for com.acme.spiffy.labelAssembly? For the sake of illustration, assume that it does not – perhaps other applications have French support, but the Spiffy application doesn't, so there aren't any com.acme.spiffy.* tokens in the French Localization Table. So we still need a value. Continuing down through the user's preferences, the next acceptable language is pt. Is there a pt localization table? No. There is a Brazilian Portuguese translation, but that won't help a user from Portugal. Still looking, we move to the next language, ru. Is there a ru Localization Table? There is. Does it contain a translation forcom.acme.spiffy.labelAssembly? It does: Ассамблея – so the token has a value, and that is what gets displayed in the UI. Suppose that the user's preferences were more specific, something like this: The users's language preference is fr-CA,pt-BR,ru-Cyrl-RU,sl-Latn-IT-nedis (Canadian French, Brazilian Portuguese, Russian in Cyrillic characters as used in Russia, Slovenian in Latin characters as used in Italy where the Natisone River dialect prevails). ThingWorx treats this by internally expanding the stack to include acceptable fall-back languages. In effect, it looks like: Of the four languages that the user can accept and that the system defines (fr-CA, fr, pt-BR, ru) the first one containing the desired token determines its value in the UI. Token and translation management for applications While it's possible to edit localized values using the Localization Table editor in Composer, translations are usually done in bulk by subject-matter experts. While workflow will vary among organizations and projects, the following example illustrates the basic process. ACME, Inc. is developing a ThingWorx application called Cambot for controlling security cameras. ACME's developer begins by constructing a mashup: This is the first draft. There is an area for the video widget, to be added later, and some button and label widgets for choosing and controlling a camera. The widgets have been given static labels: As shown here, the text for the pan left button has been entered simply as "Pan Left." But the Cambot app needs to be localized, and delivered in English, French, and Spanish. The next step for the developer is to replace all of the static text with localization tokens. Clicking the globe icon to the left of the label property changes the text from static to tokenized: and adds a magic picker for localization tokens. This is a new application, and will need its own set of localization tokens. To create the one for "Pan Left," click the magic wand to open the tokens picker: and then click "+ Localization Token" to add a new one. A dialog opens prompting for the token name and its default (English) value: Note that the token name has been namespaced for two reasons: to prevent conflicts with tokens from other sources, and to allow the developer and translators to work only with application-specific tokens. On clicking "Add Localization Token," the token is created and the default value saved. The mashup builder now shows: . After all of the tokens needed by the application have been defined, they and their values may be seen on the Localization Tokens editor for the Default Localization Table. By entering the namespace prefix in the filter textbox, the display can be restricted to the tokens for this application: As application development continues, and more tokens are required, this process is repeated. When tokens are defined, the developer should edit the Default Localization Table to supply Usage and Context information for each one: Finally, it's time to do the translations for French and Spanish. First, create the localization tables for those languages, as described above in "Create a Localization Table." From the Import/Export menu, select EXPORT / To File: Then, depending on the file format desired, choose either the Entities or Single Entity tab. For Entities, set the Collections value to Localization Tables, enter the namespace in the Token Prefix field, and choose XML as the Export Type: This will produce a single output file, containing a Localization Table element for every language defined on the system – in this example, English, French, and Spanish -- but including only the com.acme.cambot tokens. For Single Entity, choose the language to export, specify the prefix, and choose XML: This must be repeated, once for each language, and creates a separate XML file for each. In either case, the translator should be supplied with the Default XML and the file for the language to be added. (Or, the tokens and values may be converted to and from other formats, depending on the requirements of the translation service. In any case, the translated values must be in the same XML format before they can be imported.) The Default export file will contain a <Rows> element like this: < Rows >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.buttonnext]]> </ name >         < context > <![CDATA[Button to switch view to next camera]]> </ context >         < value > <![CDATA[Next Camera]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.buttonpanleft]]> </ name >         < context > <![CDATA[Button to pan view to the left]]> </ context >         < value > <![CDATA[Pan Left]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.buttonpanright]]> </ name >         < context > <![CDATA[Button to pan view to the right]]> </ context >         < value > <![CDATA[Pan Right]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.buttonprev]]> </ name >         < context > <![CDATA[Button to switch view to previous camera]]> </ context >         < value > <![CDATA[Prev. Camera]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.buttontiltdown]]> </ name >         < context > <![CDATA[Button to tilt view down]]> </ context >         < value > <![CDATA[Tilt Down]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.buttontiltup]]> </ name >         < context > <![CDATA[Button to tilt view up]]> </ context >         < value > <![CDATA[Tilt Up]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.buttonzoomin]]> </ name >         < context > <![CDATA[Button to view more detail]]> </ context >         < value > <![CDATA[Zoom In]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.buttonzoomout]]> </ name >         < context > <![CDATA[Button to expand view]]> </ context >         < value > <![CDATA[Zoom Out]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.labelcamera]]> </ name >         < context > <![CDATA[Label for current camera name]]> </ context >         < value > <![CDATA[Camera:]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[label]]> </ usage >         < name > <![CDATA[com.acme.cambot.labelrecording]]> </ name >         < context > <![CDATA[Notice displayed when camera is recording]]> </ context >         < value > <![CDATA[Recording]]> </ value >     </ Row > </ Rows > Whereas the French and Spanish export files will contain an empty <Rows/> element. This is where the new translations should be added. When the translations are ready, check that the <LocalizationTable> attributes (name, description, languageCommon, languageNative) are correct. Then import the new languages and inspect the results using the Localization Table editor. Localization tables for an application may be bundled into an extension .zip file as other entities are handled; on import, the tokens for the application will be merged with existing localization tables for the same language. In the case that a brand new language is being introduced, note that many widgets use tokens from the System localization table. These will need to be translated as well – however, there is no easy way to restrict the set of tokens to those actually used. At present this is a manual filtering step. For existing languages, check to see if the System tokens have already been translated. Important note on character encoding In handling the export, transmission and editing of XML files, it's important to ensure that UTF-8 encoding is maintained throughout. Encoding problems can show up either as errors when the file is re-imported, or as localized strings with question marks or other unexpected characters in place of accented letters. ThingWorx must run with UTF-8 as the default file encoding. Specify the Java option -Dfile.encoding=UTF-8 on launch. Windows In %CATALINA_HOME%\bin\setenv.bat, include this command:     set CATALINA_OPTS=-Dfile.encoding=UTF-8 Tips for translators Each token in an exported Localization Table XML file is defined by four fields: name, value, usage, and context. While name might be suggestive, it is actually arbitrary and should not be relied on. Value contains the natural language value for the token in another language (as agreed upon). Translating from this language into the target language is the object. Usage hints at constraints on the size of the translated text. ThingWorx widgets do not in general resize to fit contents; so a button label, column heading, field label, etc. may be more difficult to translate. Because the default language is likely to be English, and English is a particularly compact language, the application may have been designed with narrow constraints. Such tokens should be marked as tricky by having a usage value of Label. Tokens with a usage of Message are for strings in more adaptable spaces, such as a texarea, warning message, etc. Context allows the application developer to provide translation hints. This may disambiguate synonyms, explain usage, discuss space constraints, specify tone of voice, or anything else applicable. The interesting section of a language's XML representation is contained in the <Rows> element. For example: <Rows> example 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 < Rows >     < Row >         < usage />         < name > <![CDATA[com.acme.spiffy.labelPart]]> </ name >         < context />         < value > <![CDATA[Part]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[Label]]> </ usage >         < name > <![CDATA[com.acme.spiffy.labelAssembly]]> </ name >         < context > <![CDATA[Label identifying the name of the assembly being edited, appears as Assembly: external_name]]> </ context >         < value > <![CDATA[Assembly]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[Message]]> </ usage >         < name > <![CDATA[com.acme.spiffy.warningIncomplete]]> </ name >         < context > <![CDATA[Pop-up warning message on Save]]> </ context >         < value > <![CDATA[A referenced part is missing, undefined, or not allowed in this assembly.]]> </ value >     </ Row > </ Rows > In this example, the token defined in lines 2 through 7 is missing the translation cues usage and context. The translator's only option is to intuit the sense of "Part" – is it a noun or a verb? – and attempt a reasonable guess. Access to a running example of the application would clearly be helpful. Lines 8 through 13 identify a label and describe how it is used; lines 14 through 19 do the same for a message. The translator would know that space for the translation of "Assembly" might be limited but that the warning message can be expressed naturally. A translator working on French might then edit this file as follows (again, only the <Rows> element is illustrated): After translating 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 < Rows >     < Row >         < usage />         < name > <![CDATA[com.acme.spiffy.labelPart]]> </ name >         < context />         < value > <![CDATA[Partie]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[Label]]> </ usage >         < name > <![CDATA[com.acme.spiffy.labelAssembly]]> </ name >         < context > <![CDATA[Label identifying the name of the assembly being edited, appears as Assembly: external_name]]> </ context >         < value > <![CDATA[Assemblée]]> </ value >     </ Row >     < Row >         < usage > <![CDATA[Message]]> </ usage >         < name > <![CDATA[com.acme.spiffy.warningIncomplete]]> </ name >         < context > <![CDATA[Pop-up warning message on Save]]> </ context >         < value > <![CDATA[Une partie référencé est manquant, indéfini, ou non autorisés dans cette assemblée.]]> </ value >     </ Row > </ Rows > Note that only the <value> elements need to be translated – the context and usage are hints for the translator. System tokens for international data formats There are several tokens used for formatting that are also subject to localization. Token Default value Notes datepickerDayNamesMin Su,Mo,Tu,We,Th,Fr,Sa Day-of-week abbreviations used in calendar heading. datepickerFirstDay 0 First day of the week, 0 for Sunday, 1 for Monday... datepickerMonthNames January,February,March,April,May,June,July,August,September,October,November,December Month names used in calendar heading. dateTimeFormat_Default yyyy-MM-dd HH:mm:ss Date and time format codes are defined by the moment.js library. dateTimeFormat_FullDateTime LLLL dateTimeFormat_LongDate LL dateTimeFormat_LongDateTime LLL dateTimeFormat_MediumDate ll dateTimeFormat_ShortDate l dateTimeFormat_TimeOnly LT shortDateFormat mm/DD/yyyy See also KCS Article CS241828​ for details about numeric localization. Allowing users to set their own language preferences It may not be practical for the Administrator to set the language preferences for each user. An application may elect to expose the preferences editor to the end user, so that each user may select from the available languages those that are useful. To support this, ThingWorx Composer offers a Preferences widget in the Mashup builder. The widget may be inserted into any application wherever the designer chooses. It may be tied to a button or menu item, or simply appear in a layout with other widgets – perhaps along with application-specific preferences and other settings. To use the Preferences widget, design a mashup for it to appear in. The minimal case would be a responsive page mashup containing nothing but the preferences widget. Add the Preferences widget by dragging it into place: A placeholder for the widget appears in the mashup: The widget may be customized by setting various properties: These properties are specific to the Preferences widget: ShowClearRecent: Check this to include the option for the user to clear the Most Recently Used history. You may specify a localized tooltip. ShowRestoreTabs: Check this to include the option for the user to set tab restoration to ask, always, or never. You may specify a localized tooltip. ShowLanguages: Check this to include the option for the user to edit language preferences. You may specify a localized tooltip. ShowUserName: Check this to label the preferences widget with the user's name. ShowUserAvatar: Check this to label the preferences widget with the user's avatar, if one is defined. Style: Style the preferences widget itself. ButtonStyle: Style the Clear Recent and Edit buttons. These should probably be set to the application's primary button style. After adding the Preferences widget to a mashup, provide some way for the user to navigate to it, consistent with the application's UI design. The mashup may be tied to a menu entry, or assigned to a Navigation widget, or included in a page within the application's workflow – whatever suits the application design. Here is an example of providing access to preferences through a button in the application's title area: 1) The Navigation widget is placed in the page header. 2) The MashupName property is set to the mashup containing a Preferences widget. 3) The TargetWindow property is set to Modal Popup. 4) For a more interesting UI, the button label is bound from the user's name. At runtime, the example looks like this: Note that there is also a menu item leading to the mashup with the Preferences widget.

  Quickly Build Mashup Widget Extensions and Extend Application Functionality with the Eclipse Plugin.   GUIDE CONCEPT   Extensions enable you to quickly and easily add new functionality to an IoT solution. Mashup widget extensions can be utilized to enhance a user's experience, your ability to develop robust applications, and make development easier as you move forward with your IoT development.   The Eclipse Plugin for ThingWorx Extension Development (Eclipse Plugin) is designed to streamline and enhance the creation of extensions for the ThingWorx Platform. The plugin makes it easier to develop and build extensions by automatically generating source files, annotations, and methods as well as updating the metadata file to ensure the extension can be imported.   These features allow you to focus on developing functionality in your extension, rather than spend unnecessary time getting the syntax and format of annotations and the metadata file correct.     YOU'LL LEARN HOW TO   Utilized the Eclipse Plugin and Extension SDK Create and configure an Extension project Create A mashup Widget Extension Build and import an Extension   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete ALL parts of this guide is 60 minutes.     Step 1: Completed Example   Download the completed files for this tutorial: MashupWidgetSamples.zip. Download the Eclipse Plugin. Download Extensions SDK.   The MashupWidgetSamples.zip file provided to you contains a completed example of a simple Widget project and examples of more advanced widget source code. Utilize this file to see a finished example and return to it as a reference if you become stuck during this guide and need some extra help or clarification.   Keep in mind, this download uses the exact names for Entities used in this tutorial. If you would like to import this example and also create Entities on your own, change the names of the Entities you create.     Step 2: Create Mashup Widget Extension Project   First, let's get our tools installed and set. If you haven't created an extension before, see the Create An Extension guide on how to entirely configure your setup.   Download Eclipse ThingWorx SDK. Download Eclipse ThingWorx Plugin.   To create a new extensions project in the ThingWorx Extension Perspective, follow the steps below to get started:   Go to File->New->Project. Click ThingWorx->ThingWorx Extension Project.   Click Next. Enter the Project Name (for example, MyAwesomeExtension). Select Ant as your build framework. Gradle can be used if you are using a version of Eclipse that supports Gradle STS. Enter the SDK location by browsing to the directory where the Extension SDK zip is stored. Enter the Vendor information (for example, ThingWorx Labs). Change the default package version from 1.0.0 to support extension dependency. Click Next then click Finish. Your newly created project is added to the Package Explorer tab.   Creating Widgets   The ThingWorx Extensions SDK allows for easier development and in a shorter timeframe. The SDK provides steps for creating widgets, starting with an initial setup. Follow the steps below to get started on your own widget creation.   Choose the ThingWorx menu and select New Widget.   Select the parent project, in this case MyAwesomeExtension. Enter SimpleWidget for the name and A simple example of Widget creation. for description.   Click Finish.   A new folder under the /ui folder is created and contains the JavaScript and CSS files for the widget. The metadata.xml file under the configfiles directory is updated automatically. The generated JavaScript files contain a minimal implementation of the functions needed to produce a working widget.   Adding Third-Party JAR Files   There are scenarios in which a 3rd party JAR file might be required. None will be needed for this scenario, but take note of how to do it below.   Choose the Widget menu and select New Jar Resource. Select the parent project. Browse to and select the JAR file you want to add, and enter a description. Click Finish. The JAR file is added to the /lib folder and the metadata.xml file is updated automatically.   Adding Third-Party Resources and JavaScript Libraries   Third-party libraries, images, and other web artifacts needed for the widget should be placed in the /ui/<widgetname> folder or subfolders of that location. The *.ide.js and *.runtime.js files can then reference any of those artifacts via the relative path of: …/Common/extensions/<extensionname>/ui/<widgetname>/   For example, to include a third-party JavaScript library and CSS into your widget code, one would do the following: if (!jQuery().qtip) { $("body").append('<script type="text/javascript" src="../Common/extensions/MyAwesomeExtension/ui/SimpleWidget/include/qtip/jquery.qtip.js"></script>'); $("head").append('<link type="text/css" rel="stylesheet" href=" ../Common/extensions/MyAwesomeExtension/ui/SimpleWidget/include/qtip/jquery.qtip.css" />'); }     Step 3: Widget Lifecycle in the Mashup Builder   A widget has the following lifecycle stages within the Mashup Builder. During each lifecycle stage, the specified functions on the widget are called by the Mashup Builder.   Discovered   The widget is being loaded into index.html and added to the Widget toolbar/palette.   widgetProperties() - Called to get information about each widget (such as display name and description)   widgetEvents() - Called to get information about the events each widget exposes   widgetServices() - Called to get information about the services each widget exposes   Created   The widget is dragged onto a Mashup panel.   afterload() - Called after your object is loaded and properties have been restored from the file, but before your object has been rendered   Appended   The widget is appended to the workspace DOM element.   renderHtml() - Called to get an HTML fragment that will be inserted into the Mashup DOM element   afterRender() - Called after the HTML fragment representing the widget has been inserted into the Mashup DOM element and a usable element ID has been assigned to the DOM element holding the widget content. The DOM element is then ready to be manipulated.   Updated   The widget is resized or updated in the Widget property window.   beforeSetProperty() - Called before any property is updated   afterSetProperty() - Called after any property is updated   Destroyed   The widget is deleted from the mashup.   beforeDestroy() - Called right before the widget’s DOM element is removed and the widget is detached from its parent widget and deallocated. You should clean up resources (such as plugins and event handlers) acquired during the lifetime of the widget.     Step 4: Widget Coding Examples   The [widgetname].ide.js file must implement several functions to work correctly in the Mashup Builder using its API. Widgets can declare widget properties, services, and events in functions.   Mashup Builder Code   Below is sample code for a widget named SimpleWidget with a bindable string property named DisplayText. TW.IDE.Widgets.simplewidget = function () { this.widgetIconUrl = function() { return "../Common/extensions/MyAwesomeExtension/ui/simplewidget/SimpleWidget.ide.png"; }; this.widgetProperties = function () { return { name : "SimpleWidget", description : "A simple example of Widget creation.", category : ["Common"], properties : { DisplayText: { baseType: "STRING", defaultValue: "Hello, Awesome User!", isBindingTarget: true } } } }; this.renderHtml = function () { var mytext = this.getProperty('SimpleWidget Property'); var config = { text: mytext } var widgetTemplate = _.template( '<div class="widget-content widget-simplewidget">' + '<span class="DisplayText"><%- text %></span>' + '</div>' ); return widgetTemplate(config); }; this.afterSetProperty = function (name, value) { return true; }; };   Runtime Coding   To handle the widget at runtime, you need methods to do the following: Render the HTML at runtime Set up bindings after rendering the HTML Handle property updates Below is sample code of what the [widgetname].runtime.js may look like: TW.Runtime.Widgets.simplewidget = function () { var valueElem; this.renderHtml = function () { var mytext = this.getProperty('SimpleWidget Property'); var config = { text: mytext } var widgetTemplate = _.template( '<div class="widget-content widget-simplewidget">' + '<span class="DisplayText"><%- text %></span>' + '</div>' ); return widgetTemplate(config); }; this.afterRender = function () { valueElem = this.jqElement.find(".DisplayText"); valueElem.text(this.getProperty("DisplayText")); }; this.updateProperty = function (updatePropertyInfo) { if (updatePropertyInfo.TargetProperty === "DisplayText") { valueElem.text(updatePropertyInfo.SinglePropertyValue); this.setProperty("DisplayText", updatePropertyInfo.SinglePropertyValue); } }; };   Advanced Examples   If you have a local installation of the ThingWorx Composer, you can find examples of widgets in the Tomcat_Installation_Folder/webapps/Thingworx/Common/thingworx/widgets directory. DO NOT EDIT THESE FILES!. You will be able to mimic widgets you like to use them as a basis for new widgets. Or, just take notes on these items which will be covered more in-depth later in this guide.   Additional Features   You can incorporate the following features into your widgets: Services that can be bound to events (such as Click of a Button, Selected Rows Changed, or Service Completed) Events that can be bound to various services (for example, invoke a service and navigate to a mashup) Properties that can be bound out You can access the full power of JavaScript and HTML in your widget code at runtime. Anything that can be accomplished using HTML and JavaScript is available in your widget.     Click here to view Part 2 of this guide.  

1. Add an Json parameter Example: { ​    "rows":[         {             "email":"example1@ptc.com"         },         {             "name":"Qaqa",             "email":"example2@ptc.com"         }     ] } 2. Create an Infotable with a DataShape usingCreateInfoTableFromDataShape(params) 3. Using a for loop, iterate through each Json object and add it to the Infotable usingInfoTableName.AddRow(YourRowObjectHere) Example: var params = {     infoTableName: "InfoTable",     dataShapeName : "jsontest" }; var infotabletest = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape(params); for(var i=0; i<json.rows.length; i++) {     infotabletest.AddRow({name:json.rows.name,email:json.rows.email}); }

The following code is best practice when creating any "entity" in Thingworx service script.  When a new entity is created (like a Thing) it will be loaded into the JVM memory immediately, but is not committed to disk until a transaction (service) successfully completes.  For this reason ALL code in a service must be in a try/catch block to handle exceptions.  In order to rollback the create call the catch must call a delete for any entity created.  In line comments give further detail.     try {     var params = {         name: "NewThingName",         description: "This Is A New Thing",         thingTemplateName: "GenericThing"     };     Resources["EntityServices"].CreateThing(params);    // Always enable and restart a new thing to make it active on the Platform     Things["NewThingName"].Enable();     Things["NewThingName"].Restart();       //Now Create an Organization for the new Thing     var params = {         topOUName: "NewOrgName",         name: "NewOrgName",         description: "New Orgianization for new Thing",         topOUDescription: "New Org Main"     };     Resources["EntityServices"].CreateOrganization(params);       // Any code that could potentially cause an exception should     // also be included in the try-catch block. } catch (err) {     // If an exception is caught, we need to attempt to delete everything     // that was created to roll back the entire transaction.     // If we do not do this a "ghost" entity will remain in memory     // We must do this in reverse order of creation so there are no dependency conflicts     // We also do not know where it failed so we must attempt to remove all of them,     // but also handle exceptions in case they were not created       try {         var params = {name: "NewOrgName"};         Resources["EntityServices"].DeleteOrganization(params);     }     catch(ex2) {//Org was not created     }       try {         var params = {name: "NewThingName"};         Resources["EntityServices"].DeleteThing(params);     }     catch(ex2) {//Thing was not created     } }

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

This document is designed to help troubleshoot some commonly seen issues while installing or upgrading the ThingWorx application, prior or instead of contacting Tech Support. This is not a defined template for a guaranteed solution, but rather a reference guide that provides an opportunity to eliminate some of the possible root causes. While following the installation guide and matching the system requirements is sufficient to get a successfully running instance of ThingWorx, some issues could still occur upon launching the app for the first time. Generally, those issues arise from minor environmental details and can be easily fixed by aligning with the proper installation process. Currently, the majority of the installation hiccups are coming from the postgresql side. That being said, the very first thing to note, whether it's a new user trying out the platform or a returning one switching the database to postgresql, note that: Postgresql database must be installed, configured, and running prior to the further Thingworx installation. ThingWorx 7.0+: Installation errors out with 'failed to succeed more than the maximum number of allowed acquisition attempts' Platform being shut down because System Ownership cannot be acquired error ERROR: relation "system_version" does not exist Resolution: Generally, this type of error point at the security/permission issue. As all of the installation operations should be performed by a root/Administrator role, the following points should be verified: Ensure both Tomcat and ThingworxPlatform folders have relevant read/write permissions The title and contents of the configuration file in the ThingworxPlatform folder has changed from 6.x to 7.x Check if the right configuration file is in the folder Verify if the name and password provided in this configuration file matches the ones set in the Postgres DB Run the Database cleanup script, and then set up the database again. Verufy by checking the thingworx table space (about 53 tables should be created)     Thingworx Application: Blank screen, no errors in the logs, "waiting for <url> " gears running be never actually loading, eventually times out     Resolution: Ensure that Java in tomcat is pointing to the right path, should be something like this: C:\Program Files\Java\jre1.8.0_101\bin\server\jvm.dll 6.5+ Postgres:   Error when executing thingworxpostgresDBSetup.bat psql:./thingworx-database-setup.sql:1: ERROR: could not set permissions on directory "D:/ThingworxPostgresqlStorage": Permission denied     Resolution:     The error means that the postgres user was not able to create a directory in the ‘ThingworxPostgresStorage’ directory. As it's related to the security/permission, the following steps can be taken to clear out the error: Assigning read/write permissions to everyone user group to fix the script execution and then execute the batch file: Right-click on ‘ThingworxPostgresStorage’ directory -> Share with -> specific people. Select drop-down, add everyone group and update the permission level to Read/Write. Click Share. Executing the batch file as admin. 2. Installation error message "relation root_entity_collection does not exist" is displayed with Postgresql version of the ThingWorx platform. Resolution:     Such an error message is displayed only if the schema parameter passed to thingworxPostgresSchemaSetup.sh script  is different than $USER or PUBLIC. To clear out the error: Edit the Postgresql configuration file, postgresql.conf, to add to the SEARCH_PATH item your own schema. Other common errors upon launching the application. Two of the most commonly seen errors are 404 and 401.  While there can be a numerous reasons to see those errors, here are the root causes that fall under the "very likely" category: 404 Application not found during a new install: Ensure Thingworx.war was deployed -- check the hard drive directory of Tomcat/webapps and ensure Thingworx.war and Thingworx folder are present as well as the ThingworxStorage in the root (or custom selected location) Ensure the Thingworx.war is not corrupted (may re-download from the support and compare the size) 401 Application cannot be accessed during a new install or upgrade: For Postgresql, ensure the database is running and is connected to, also see the Basic Troubleshooting points below. Verify the tomcat, java, and database (in case of postgresql) versions are matching the system requirement guide for the appropriate platform version Ensure the updrade was performed according to the guide and the necessary folders were removed (after copying as a preventative measure). Ensure the correct port is specified in platform-settings.json (for Postgresql), by default the connection string is jdbc:postgresql://localhost:5432/thingworx Again, it should be kept in mind that while the symptoms are common and can generally be resolved with the same solution, every system environment is unique and may require an individual approach in a guaranteed resolution. Basic troubleshooting points for: Validating PostgreSQL installation Postgres install troubleshooting java.lang.NullPointerException error during PostgreSQL installation ***CRITICAL ERROR: permission denied for relation root_entity_collection Error while running scripts: Could not set permissions on directory "/ThingworxPostgresqlStorage":Permission Denied Acquisition Attempt Failed error Resolution: Ensure 'ThingworxStorage', 'ThingworxPlatform' and 'ThingworxPostgresqlStorage' folders are created The folders have to be present in the root directory unless specifically changed in any configurations Recommended to grant sufficient privileges (if not all) to the database user (twadmin) Note: While running the script in order to create a database, if a schema name other than 'public' is used, the "search_path" in "postgresql.conf" must be changed to reflect 'NewSchemaName, public' Grant permission to user for access to root folders containing 'ThingworxPostgresqlStorage' and 'ThingworxPlatform' The password set for the default 'twadmin' in the pgAdmin III tool must match the password set in the configuration file under the ThingworxPlatform folder Ensure THINGWORX_PLATFORM_SETTINGS variable is set up Error: psql:./thingworx-database-setup.sql:14: ERROR:  could not create directory "pg_tblspc/16419/PG_9.4_201409291/16420": No such file or directory psql:./thingworx-database-setup.sql:16: ERROR:  database "thingworx" does not exist Resolution: Replacing /ThingworxPostgresqlStorage in the .bat file by C:\ThingworxPostgresqlStorage and omitting the -l option in the command window. Also, note the following error Troubleshooting Syntax Error when running postgresql set up scripts

This is using the simplest structure to do a look through an infotable.  It's simple but it avoids having to use row indexes and cleans up the code for readability as well.   //Assume incoming Infotable parameter names "thingList" for each (row in thingList.rows) {      // Now each row is already assigned to the row variable in the loop      var thingName = row.name; }   You can also nest these loops (just use a different variable from "row").  Also important to note to not add or remove row entries of the Infotable inside the loop.  In this case you may end up skipping or repeating rows in the loop since the indexes will be changed.

Developing Great IoT Solutions Brought to you once again by your EDC team, find attached here a brand-new, comprehensive overview of ThingWorx best practices! This guide was crafted by combining all available feedback, from support cases to PTC Community threads, and tapping all internal resources. Let this guide serve to bridge the knowledge gaps ThingWorx developers most commonly see.    The Developing Great IoT Solutions (DGIS) Guide is a great way to inform both business and technically minded folks about the capabilities of the ThingWorx Platform. Learn how to design good solutions from a high-level, an overview designed specifically with the business audience in mind. Or, learn how to implement good IoT designs through a series of technical examples. Start from very little knowledge of the Platform and end up understanding data structures and aggregation, how to use the collection widget, and how to build a fully functional rules engine for sending and acknowledging alerts in ThingWorx.   For the more advanced among us, check out the Appendix. Find here a handy list of do's and don'ts surrounding ThingWorx best practice in development, with links to KCS, Help Center, and Community content.   Reinforce your understanding of the capabilities of the ThingWorx Platform with this guide, today!   A big thanks to all who were involved on this project! Happy developing!

Create a new Thing using the Scheduler Thing Template. The Scheduler Thing will fire a ScheduledEvent Event when the configured schedule is fired. The event is automatically present and does not need to be added manually. Configuration   The Scheduler Configuration is quite straightforward and allows for an exact setup of schedule based on units of time, e.g. seconds, minutes, hours, days of week etc. It can be accessed via the Thing's Entity Configuration   Configuration allows for Changing the runAsUser context - in which the Events will be handled. The user will need visibility and permission on e.g. executing Services or depending Things, which are required to run the Service triggered by the Event. Changing the Schedule - in which time the Events will be fired (by default every minute). The schedule is displayed in CRON String notation and can be changed and viewed in detail by clicking on "More". The CRON String will be generated automatically based on the inputs. Schedules can be configured in Manual mode - allowing for full configuration of each and every time based attribute. Schedules can be configured for a specific time Type - allowing for configuration only based on seconds, minutes, hours, days, weeks, months or years. Below screenshots show schedules running every minute and every Saturday / Sunday at 12:00 ("Every Weekend Day").     Services   Scheduler Things inherit two Services by default from the Thing Template DisableScheduler EnableScheduler These will activate / de-activate the Scheduler and allow / disallow firing Events once a scheduled time is reached If a Scheduler is currenty enabled or disabled can be seen in its properites  

Business logic and actions in a ThingWorx application are driven by events.  Events are interesting or critical property states that a Thing publishes to subscribers.  Events are defined at the thing, thing template, or thing shape level, and can be as simple as a new data value from a device, to complex events from many data points.  An event that is created on a thing shape or thing template level is inherited down into the entities that implement the shape or template that defines the event.   Types of Events Built-in Custom   Built-in Events Every entity in ThingWorx can use several built-in events.  These events are automatically triggered when a prerequisite condition is met.  In some cases, you must provide information that determines the specifics of that prerequisite.   Common built-in events include: Data Change Alert Timer  In ThingWorx, there are standard events and related data packets (defined by Data Shapes).  The most common type of event is a Data Change related to a Thing property.  The Data Change fires when a property value changes. The specifics of this event can be configured from the Data Change Info section of the Property Creation page.  When you define a property, there are many configuration aspects.   Example:   Using DataChangeEvent, you have a few options for setting an event to fire when there is new data for a property: only if the data has changed only if the data evaluates true or false only if the new value changed beyond a defined threshold   The Alert event is also attached to a property and is set up from the Property Definition page.  An Alert event fires when the value of the property reaches a certain threshold or value.  Depending on the base type, you may specify the condition of this event from the Manage Alerts button.   Timer events can be used to run jobs or fire events on a regular basis.  Things themselves can fire specific events such as Thing Start or “special” things, like a Timer type thing that contains special event(s).   How to Set up and Configure Timers   Creating a Custom Event To create a new custom event:   In ThingWorx Composer, open up the Thing, Thing Template, or Thing Shape for edit where the new Event will be added From the sidebar menu under Entity Information, select Events Click Add My Event Provide the event with a name and a data shape that describes the data being sent to a subscriber Click Done and Save   Once created, the event appears on the Subscriptions page for subscribing.  Since the event is custom built, you must specify when it triggers.  This is often accomplished from within a service.   Best Practices There should be a subscriber to the event in the model. The subscriber is sent a data packet and the subscription is initiated. If no one is subscribed to the event (no one is listening), nothing happens. DataChange events hit the event processing subsystem (see Event Processing Subsystem below), which has fewer threads allocated to it than some other subsystems: If queries within subscriptions to data change events take too long, this will cause a back-up in the event processing subsystem Too much activity in this subsystem may result in poor system performance, and even server crashes Be cautious what types of calculations are performed in data change event subscriptions Refer to the following knowledge base article regarding tricky data change event use cases, the implementation of rules for alerts, and conditional operations:  Complicated Event Calculations in ThingWorx Load Test all applications in Sandbox before committing changes to Production to ensure mashups can handle all design choices on large scales   Event Processing Subsystem The Event Processing Subsystem manages event processing for external subscriptions (Things subscribing to other Things) throughout ThingWorx.   Event Queue Processing Settings Base Type Default Notes Min Threads Allocated to Event Processing Pool NUMBER 16   Max Threads Allocated to Event Processing Pool NUMBER 500   Max Queue Entries Before Adding New Working Thread NUMBER 200000 Maximum number of entries to queue before adding a thread to the pool     Additional Information PTC ThingWorx Help Center - Thing Events Event Types Creating and Triggering Custom Events in ThingWorx  

A license is not required to download and install KEPServerEX V6. After finishing the full install, all features of the KEPServerEX Configuration are available for setup and testing, including the ThingWorx Native Interface. When either a Client application makes a request of a Driver, or a Plug-in becomes activated, then a license check is performed. If a feature is not licensed, a two-hour demo countdown period will begin for that feature. There are System tags that can be monitored in KEPServerEX to determine which features are licensed, which are in a time-limited demo countdown period, and which have expired. To reset the countdown timer for an unlicensed feature, the KEPServerEX Runtime service can be stopped/restarted. See the links and steps below for more information. How do I download and install KEPServerEX?​ Monitor licensed, time-limited, and expired features in KEPServerEX:​ 1. Open the KEPServerEX Configuration window 2. The right-most icon in the Configuration icon toolbar is the white "QC" icon. Select this icon to launch a new OPC Quick Client window. The server project should automatically build 3. In the left pane, locate the _System folder 4. Within this folder locate the following three Tags, with the corresponding information displaying in the Value column: Tag (Item ID) Information displayed as a String in the Value column _System._ExpiredFeatures Any unlicensed driver or plug-in that has timed out _System._LicensedFeatures Drivers or plug-ins that are currently licensed for use with this install _System._TimeLimitedFeatures Any unlicensed driver or plug-in that is in demo mode, with the remaining demo timer countdown (in seconds) Note: The ThingWorx Native Interface does not require a KEPServerEX license. Restart the KEPServerEX Runtime service The KEPServerEX Runtime service (server_runtime.exe) runs in the background as a system service. Resetting this service will reset the demo countdown for any unlicensed feature. To reset the Runtime service: 1. Right-click on the Administration tool (the green EX icon in the Systray by the System Clock) 2. Select "Stop Runtime Service. 3. Either the user will be prompted to restart this service, or the same Administration tool menu can be used again to "Start Runtime Service"

I have created a mashup which allows you to easily use and test the Prescriptions functionality in Thingworx Analytics (TWA). This is where you choose 1 or more fields for optimization, and TWA tells you how to adjust those fields to get an optimal outcome.   The functionality is based on a public sample dataset for concrete mixtures, full details are included in the attached documentation.  

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