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IoT & Connectivity Tips

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Key Functional Highlights ThingWorx Manufacturing Apps enhancements Support for NI InsightCM connected to KEPServerEX as an aggregator Controls Advisor usability improvement to retrieve App Key for a specific KEPServerEX connection Asset Advisor usability improvement for displaying alerts Compatibility ThingWorx 8.1.0 KEPServerEX 6.2, 6.3 KEPServerEX V6.1 and older as well as different OPC Servers (with Kepware OPC aggregator) Documentation ThingWorx Manufacturing Apps Setup and Configuration Guide ThingWorx Manufacturing Apps Customization Guide What’s New in ThingWorx Manufacturing Apps 8.1.0 Download Extensions for ThingWorx Manufacturing Apps and Asset Remoting Note: this release announcement applies to the ThingWorx Manufacturing Apps Extensions 8.1.0. For the ThingWorx Manufacturing Apps Freemium (Express) 8.1.0 release notes, see this page: ThingWorx Manufacturing Apps 8.1 Freemium is Available for Download!
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Procedure to configure a secure connection between Windchill and Thingworx server. Assuming Windchill and Thingworx are already configured with SSL, this video consists of detailed steps for setting up Thingworx and Windchill to trust each other.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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Introduction to the base EMS connections and settings, what and how the websocket connections work, security, data transfer and bandwidth.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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Introduction to the platform extensibility structures and options. Includes overview of setting up the eclipse plugin and build process, as well as install considerations and best practices.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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This design session introduces a real-world product scenario along with requirements for developing a related IoT-based application. You will also be introduced to core ThingWorx terminology and concepts that will help to map out an efficient design plan for the model hierarchy.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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This Expert Session consists of the general overview for the multitenancy and platform security. It  discusses the available security levels, necessary basic resources, as well as provides information on the system user, and also includes several examples on how-to. It’s assumed that the audience is familiar with the Composer and its navigation.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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This Expert Session consists of the general overview for platform export and import. It discusses the available options for safely exporting and importing entities, data, and extensions. It also provides information on the use of exported entities during the system upgrading and/or moving from QA to production server.  It’s assumed that the audience is familiar with the Composer and its navigation.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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Finally there is an article which combines all of the available resources on certificate configuration to better enable developers to complete their production-worthy edge devices. Please see the official PTC documentation located here. Please feel free to comment with any questions, comments, or feedback on this! Happy developing!
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The following Expert Session videos are now available for viewing within the ThingWorx Community: ThingWorx Analytics Installation - This Expert Session will walk you through the complete installation of ThingWorx Analytics from the Prerequisites to Confirming the Installation is successful and all steps in between. The first half of the video gives a breakdown of the components and the process of the installation with the second half being an actual Demo of the Installation.     ThingWorx Analytics API Overview - This Expert Session is designed to help beginners get up and running with ThingWorx Analytics. It covers basic concepts like: What are APIs, how to configure the metadata file, and a live Demo that shows you how to interact and use ThingWorx Analytics in real time. This Expert Session would also be useful for experienced users who need a refresher course.   Decision Tree, ThingWorx Analytics Builder - This Expert Session reviews the concept of “Decision Trees” and the functionality that is available in ThingWorx Analytics Builder. First, you will learn how to create and upload a dataset in ThingWorx Analytics Builder.  After that, it shows you how to train a model and score on the model that was just generated. It then goes into detail on how the prediction learner "Decision Tree" operates and classifies inputs.   Use Case Identification - This Expert Session goes over ways to identify and develop a successful use case for ThingWorx Analytics. The example use case presented here is on employee retention in a fictional company with the goal of maximizing employee retention . This presentation will provide you with all the fundamentals you need to develop your own ThingWorx Analytics use cases from the ground up.   ThingWorx Analytics Signals - This Expert Session will provide you with an in depth explanation behind how Signals are calculated in ThingWorx Analytics, what purpose they serve, and why we use them.  Some basic mathematical concepts are discussed so viewers will have a better idea of how ThingWorx Analytics operates behind the scenes.   Related Links For more information, you can visit a new space dedicated to these helpful technical videos.   Additional Expert Sessions will be highlighted here in the ThingWorx Community every few weeks. Visit the Online Success Guide to access additional information about ThingWorx training and services.
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This Expert Session will provide you with an in depth explanation behind how Signals are calculated in ThingWorx Analytics, what purpose they serve, and why we use them.  Some basic mathematical concepts are discussed so viewers will have a better idea of how ThingWorx Analytics operates behind the scenes.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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This Expert Session goes over ways to identify and develop a successful use case for ThingWorx Analytics. The example use case presented here is on employee retention in a fictional company with the goal of maximizing employee retention . This presentation will provide you with all the fundamentals you need to develop your own ThingWorx Analytics use cases from the ground up.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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This Expert Session reviews the concept of “Decision Trees” and the functionality that is available in ThingWorx Analytics Builder. First, you will learn how to create and upload a dataset in ThingWorx Analytics Builder.  After that, it shows you how to train a model and score on the model that was just generated. It then goes into detail on how the prediction learner "Decision Tree" operates and classifies inputs.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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ThingWorx's JDBC extensions - Relational Database Management System and the JDBC Extensions allow ThingWorx to connect to variety of different databases. With that comes a natural question how and what sort of SQL statements could be executed via these extensions? Note: ​​Importing the JDBC extensions i.e. the RDBMS and JDBC Extensions, creates a Database Template for that particular database. If you are working with RDBMS extension then Template of corresponding Database will be created with similar name e.g. importing the RDBMS Extension for Oracle 12 will create Template named OracleDBServer12. While importing the JDBC driver using the JDBC extension will create Template name based on the JDBC driver used or a custom name could be given. Following examples and SQL statements are adhering to Oracle's SQL*Plus standard, however these can be easily adapted to the type of RDBMS you intend to work with. Topics How to create SQL Service in ThingWorx entity Types of SQL Statements Examples on SQL Service usage and some extended use cases / examples How to create SQL Service in ThingWorx Navigate to the Thing implementing the Database Template, e.g. OracleDBServer12         2. Click on the Services section under the Entity Information and click on Add My Service         3. A new service creation section will come up, change the Service type of JavaScript (this is default selection) to either SQL (Query) or SQL (Command) depending on the type of SQL you are to create under this particular service                       4. Here's quick example on creating SQL (Query) service which takes name as input for a select *  sql … Statement, i.e. it returns complete set of rows and columns from any given table on which the user has the access to perform Select                   Note: BaseType defaults to Infotable when creating SQL (Query) service and the returned number of rows are restricted to 500. Therefore, if table contains rows more than 500, ensure to change the Max Rows parameters         5. Example on creating SQL (Command) service that delete all the rows from the database table               Note: The Base Type defaults to Number when using SQL (Command)     Additional information:     When creating a SQL service, apart from providing changing the Service Info and  Inputs /Outputs, 3rd section Tables/Columns allows users to explore the Tables and their respective columns as part of that particular user's schema - meaning the objects on which the user has select rights in his schema in the database.     Types of SQLs This is not an exhaustive list, rather contains most commonly used types of SQL statements     1. Data Definition Language (DDL)           a. Create, Alter and drop schema objects           b. Grant and Revoke privileges and roles     2. Data Manipulation Language (DML)           a. Insert           b. Delete           c. Select Examples for SQL Service usage and some extended use cases / examples     1. Data Definition Language (DDL)           a. Create statement                       b. Alter statements                         c. Drop statement                         d. Flashback statements (Oracle specific)                         e. Grant statement                     f. Rename statement                 2. Data Manipulation Language (DML)           a. Insert statement                     b. Delete statement                     c. Select statements           Use cases - Case 1 : Backing up DataTable DataTable objects in ThingWorx are for quick lookup of data and they are most performant till ~100K rows. Exceeding rows over 100K in a DataTable makes it highly susceptible to performance issues in terms of querying or writing to it. Unless, there's sharding​ on the persistence provider or multiple persistence providers used - JDBC connectivity to external data stores like RDBMS systems could help in keeping up with growing number of rows in DataTables. RDBMS tables are more than capable of storing very large amount of rows without being taxed over the performance. JDBC extension could be used to do just that in a use case requiring backing up DataTable or any Data Storage objects from ThingWorx for that matter. Here's one quick example using one of the Insert SQL service shown above to back up the entire DataTable to the Oracle's DB table. Following ThingWorx JavaScript service wraps the InsertIntoBULKDATAINSERTDT SQL service: // result: INTEGER // getting total row count in the DataTable var totalCount = Things["BulkInsertDT"].GetDataTableEntryCount(); var params = { maxItems: totalCount /* NUMBER */ }; // result: INFOTABLE // DataTable service to fetch all the rows from it var allData = Things["BulkInsertDT"].GetDataTableEntries(params); // looping over the result fetched above to get all the rows for insertion     for (var i = 0; i<totalCount; i++) {         var result = allData.getRow(i); // mapping the data for insert     var params = {         LongCol3: result.LongCol3 /* LONG */,         numcol1: result.NumCol1 /* NUMBER */,         StringCol2: result.StringCol2 /* STRING */,         IntCol4: result.IntCol4 /* INTEGER */     }; try { // result: NUMBER // calling the SQL Service InsertIntoBULKDATAINSERTDT created under a DB Thing called OracleDBThingNew     var result = Things["OracleDBThingNew"].InsertIntoBULKDATAINSERTDT(params); } catch (err) {      Logger.info ("Failed to insert the values" + err) }     }
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In the last while I've seen a few things which got me thinking about how value is created or unlocked from connected data.  Multiple components are required to create, send, store and manage the data created by edge devices, doing these things enables value to be unlocked, but what does it take to unlock the value? It was this ​article which first got me interested in considering this question.  In particular it was a section near the bottom of the article where the author describes a number of creative business use cases for car manufacturers which could be enabled by connected data.  If this author could come up with several creative and potentially valuable use case examples for one industry I started to wonder what other sorts of use cases could exist in other industries?  Could there be a series of use of use cases which a little variation be applied to different industries? The second article which further sparked my interest in where the value originates from, is this one​ on using a cryptocurrency with IOT.  While the idea of using a blockchain like technology with IOT is intriguing, it was the second image in the article (below) which resonated with me on value.  This image is a graphical representation of the connection between the key components of a connected system and it makes it clear that each component has a critical role to play and the whole system and missing anyone of the parts and the system doesn't function.  This image make it clear that it's the "Analyze" phase which drives the action to do something, and it's taking an action which is the reason the systems reason for existing.    Which brings me to the third and final article describing Industry 4.0.  Like the other two articles, it wasn't the main point of the article I found most interesting, rather it was the image below, and in particular the side bar 'Value Creation through' which brought me back to the question of where value comes from.  The idea that in a manufacturing setting, value can be created through product or process innovations as well as through new business models is intriguing.  I think a fourth idea missing from this list, is one were network effects from getting more and more proprietary data creating a compounding effect, like with Facebook or LinkedIn.  If there are at least four modes of value creation, maybe there others? While these articles caused me to ask some questions, none of them really answered the question of where the value is unlocked. To answer the question I decided to restate the question to be "how is value unlocked from data" making the assumption the value is derived from the data.  This question is a little easier to address.  The best visual representation of the answer I've seen is the data value road map (below) from the Creating a Data-Driven Organization book which was released a couple of years ago.  While I think the author is probably missing at least two boxes above 'optimization' ("new business models" and "data driven network effects") I think the graphic does a good job communicating that as the value created from data increases, the complexity of the analytic task also increases; suggesting the value is unlocked by the analytics.  For me, the value from a system of connected devices is unlocked from the "analysis" phase as seen in the first image. But in order to perform the "analysis" I think requires two things.  First asking the right high value questions of the data (product managers/beginning with the end in mind/use cases) and then using the right set of technologies to address those questions which in many instances means Artificial Intelligence of some sort.  Interestingly although artificial intelligence is required for many high value use cases, both parts of the analysis require distinctly human skills (the right use cases & controlling the technology) to create externalized intelligence and generate value. Creating a Data-Driven Organization: Practical Advice from the Trenches 1, Carl Anderson, eBook - Amazon.com
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Key Functional Highlights ThingWorx 8.1 covers the following areas of the product portfolio: ThingWorx Analytics, ThingWorx Utilities and ThingWorx Foundation which includes Core, Connection Server and Edge capabilities. Highlights of the release include: ThingWorx Foundation Next Generation Composer: Embedded Mashup Builder enables codeless development of web visualization. New ability to manage and push configurations for KEPServerEX Notifications: Create SMS and Email notifications natively in Next Generation Composer Support for localized and dynamic content with tokens Protocol Adapter Toolkit: Encrypted communication between edge devices and the Connector over HTTPS or WSS. Encrypted communication between the Connector and ThingWorx Core (WSS). Ability to define a mapping of outbound messages from ThingWorx Core using a Codec to an edge-bound message. Authentication of edge devices 3 rd Party Platform Connectivity: Azure IoT Connector v2.0 § Model data from Azure IoT using the Thing Model § Utilize data from Azure IoT as properties in the Thing Model § Utilize services and events through Azure IoT § Utilize Azure file storage ThingWorx for Predix v1.0 § Synchronize data from Predix to ThingWorx § Enable SSO between Predix & ThingWorx C SDK: Framework for custom functionality to be added to C SDK-based applications at runtime License Management: Simple, automated, licensing system for collection, storage, reporting, management and auditing of licensing entitlements. Deprecated the SQUEAL functionality ThingWorx Analytics Categorical and Ordinal Goals: Adds use of unordered text (categorical) and ordered text (ordinal) goals to predictive analytics.  Create and score models with the new goal types. Virtual Sensor: Adds support for time-series predictions when historical data is not available.  Allows machine learning predictions to take the place of physical sensors and simplifies predictions like time to failure and probability of failure. Tighter platform integration: Analytics Server is more tightly integrated with ThingWorx Core, providing native control and access to analytics programming interfaces. New, simplified API: The new Analytics Server 2.0 API pattern is simpler, more modern, and easier to use. Microservices-based Architecture: Conversion to microservices sets the stage for High Availability and improved distributed installations. Native Linux installer: Docker is no longer required to run on Linux-based systems. Analytics Manager: Several enhancements, including: Simulation-driven data framework allows external providers to send data as if they were a physical Thing. Time Series Data Inputs improves the ability to share time series data with external providers. Thing Connect / Disconnect makes it easier to connect specific Things with external providers. Analytics Builder: Ease of use enhancements including: New UI support for time series models. Easier access to / use of Signals and Profiles. Simplified models for Boolean goals. Easier installation, no longer requires UploadThing. ThingWorx Utilities Software Content Management (SCM): Define package dependencies where the deployment of a package requires the presence of one or more other packages. ThingWorx Trial Edition ThingWorx Trial Edition will be available to internal PTC resources at launch and will be made available externally on the Developer Portal shortly after launch. Developer Enablement: Enhancements have been made to the Trial Edition installation tool, providing a native installation process of the ThingWorx platform including: ThingWorx Foundation ThingWorx Utilities ThingWorx Analytics ThingWorx Industrial Connectivity Documentation ThingWorx 8.1 Reference Documents ThingWorx Analytics 8.1 Reference Documents ThingWorx Core 8.1 Release Notes ThingWorx Core Help Center ThingWorx Edge SDKs and WebSocket-based Edge MicroServer Help Center ThingWorx Connection Services Help Center ThingWorx Industrial Connectivity Help Center ThingWorx Utilities Help Center ThingWorx Utilities Installation Guide ThingWorx Analytics Help Center ThingWorx Trial Edition User Guide Additional information ThingWorx eSupport Portal ThingWorx Developer Portal ThingWorx Marketplace Download The following items are available for download from the PTC Software Download site. ThingWorx Platform – Select Release 8.1 ThingWorx Utilities – Select Release 8.1 ThingWorx Analytics – Select Release 8.1
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Starting with the 8.1 release, the architecture of ThingWorx Analytics has changed from being a single sever to being split into several independent microservices.  This has been done to allow services to run concurrently. It also prevents issues with one microservice from affecting the others. Overview The new Analytics Server Architecture consists of a suite of 9 microservices: Data Clustering Profiling Signals Training Prediction Validation Presciptive Results All of the microservices work together to create a similar experience for users as it was in the past. The data that is uploaded and generated by the Analytics Server is stored directly in a file system, instead of a Postgres Database like it was in the past. Closer Integration with ThingWorx Please note that ThingWorx Foundation is required to be installed and operating before Installing Analytics.  During the install you will be asked to supply IP Address of the ThingWorx Instance that will be used for Analytics.  At this step, the AnalyticsServerThing is configured which allows the user to interact with Analytics Server through ThingWorx.  All of the configured microservices are represented as Things under the AnalyticsServerThing. This is because ThingWorx Analytics has become a native part of ThingWorx Foundation functionality and is dependent on ThingWorx for user interaction.  Because of these changes, there is no longer a direct ThingWorx Analytics Server REST API. Support for accessing the services via REST calls is now provided through the ThingWorx Core REST API layer.  Because of this, a new URI pattern is required moving forward. One other update from the older versions is that the requirement to use application keys and Application IDs are no longer necessary.  This should come as a welcome relief as the Application keys and IDs were the source of issues for users who may have misplaced them etc. Less Data-Centric In the old versions, jobs, models, signals, etc. were all tied to the dataset.  So there was no way to a model from one dataset to the other. With the new architecture, this is no longer the case you are able to move a model from one dataset to the other seamlessly.  Please note that when moving a model from one dataset to the other, it must have the same metadata between each of the datasets.  This is because a model created to increase efficiency in a factory would provide no insight on a dataset that monitors the soil moisture in a corn field. Updates to Metadata Although going over the exact changes to the Metadata is out of scope for this post, it is worth mentioning. For more details on the changes, please follow this link. Summary In conclusion, the new architecture of ThingWorx Analytics was done to increase scalability and to produce a more robust system.  The new release is much more integrated into the ThingWorx Platform to increase the ease of use from the previous releases.  It is much less data-centric than it was in the past and geared more to the solutions themselves. 
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This video covers the new features of ThingWorx Analytics Builder 8.1   Updated Link for access to this video: What's New in ThingWorx Analytics Builder 8.1
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We are pleased to announce that the Expert Sessions video series is now available in the ThingWorx Community. We are kicking off this availability with a new space dedicated to these helpful technical videos. In the first round of videos, we are highlighting two ThingWorx Foundation videos that are designed to provide foundational knowledge to get you up and running on the ThingWorx IoT platform. New Expert Sessions Available Now ThingWorx Foundation - Installation is an introduction to installing the ThingWorx platform. The video includes information on the environment, prerequisites, and configuration steps when installing ThingWorx, and includes walkthroughs of installing with H2 and PostgreSQL databases, an introduction and demonstration of the Linux installation script, solutions to common installation problems and more. ThingWorx Foundation - Scalability talks about platform sizing with dependency on the type of environment and correlated scalability options. The video educates you about federation and high availability as well as provides visual diagrams to understand the architecture of different ThingWorx solutions. What is an Expert Session? Expert Sessions are focused, technical webcasts (both recorded and live) where PTC subject matter experts share knowledge and best practices on topics related to the design, development, deployment and operation of PTC software. Expert Sessions are designed using five categories: Get Started, Design, Develop, Deploy, and Operate. Additional Expert Sessions will be highlighted here in the ThingWorx Community every few weeks. Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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Parquet Data Format used in ThingWorx Analytics   Starting ThingWorx Analytics Version 8.1 Data storage will no longer require the installation of a PostgreSQL database. Instead, uploaded CSV data is converted to the optimized  Apache Parquet format and stored directly in the file system. This Blog explains some the features of Apache Parquet justifying this transition in ThingWorx Analytics Data Storage. features What is Apache Parquet: Apache Parquet is a column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Below is an illustration of the Columnar Storage model: Apache Parquet Features and Benefits: Apache Parquet is implemented using the record shredding and assembly algorithm taking into account the complex data structures that can be used to store the data. Apache Parquet stores data where the values in each column are physically stored in contiguous memory locations.  Due to the columnar storage, Apache Parquet provides the following benefits: Column-wise compression is efficient and saves storage space Compression techniques specific to a type can be applied as the column values tend to be of the same type Queries that fetch specific column values need not read the entire row data thus improving performance Different encoding techniques can be applied to different columns Some advantages of using Parquet for ThingWorx Analytics: Apart from the above benefits of using Parquet which amount to higher efficiency and increased performance, below are some advantages that apply specifically to ThingWorx Analytics This change in ThingWorx Analytics from using a Database to using Parquet removes the limitations on the number of data columns the system can handle. It also allows for streamlining the dataset creation process. Since the data is converted to a Parquet format, there is no need to separately optimize the dataset. Even when new data is appended to an existing dataset, a new partition is added and re-optimization is optional but not required. Data could be appended easily so there is no longer a need to re-load the full Dataset when new Data values are added The illustration below shows the transition from Row-based Data Storage model VS the columnar based Storage of Parquet
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