IoT Tips

First we need to Understand below terms: Quantitative Variable: A quantitative variable is naturally measured as a number for which meaningful arithmetic operations make sense. Examples: Height, age, crop yield, GPA, salary, temperature, area, air pollution index (measured in parts per million), etc. Categorical variable: Any variable that is not quantitative is categorical. Categorical variables take a value that is one of several possible categories. As naturally measured, categorical variables have no numerical meaning. Examples: Hair color, gender, field of study, college attended, political affiliation, status of disease infection. Ordinal Variables: An ordinal variable is a categorical variable for which the possible values are ordered. Ordinal variables can be considered “in between” categorical and quantitative variables. Example: Educational level might be categorized as     1: Elementary school education     2: High school graduate     3: Some college     4: College graduate     5: Graduate degree •    In this example (and for many ordinal variables), the quantitative differences between the categories are uneven, even though the differences between the labels are the same. (e.g., the difference between 1 and 2 is four years, whereas the difference between 2 and 3 could be anything from part of a year to several years) •    Thus it does not make sense to take a mean of the values. •    Common mistake: Treating ordinal variables like quantitative variables without thinking about whether this is appropriate in the particular situation at hand. Ordinal regression: In statistics, ordinal regression (also called "ordinal classification") is a type of regression analysis used for predicting an ordinal variable. The Ordinal Regression procedure allows you to build models, generate predictions, and evaluate the importance of various predictor variables in cases where the dependent (target) variable is ordinal in nature. Ordinal dependents and linear regression: When you are trying to predict ordinal responses, the usual linear regression models don't work very well. Those methods can work only by assuming that the outcome (dependent) variable is measured on an interval scale. Because this is not true for ordinal outcome variables, the simplifying assumptions on which linear regression relies are not satisfied, and thus the regression model may not accurately reflect the relationships in the data. In particular, linear regression is sensitive to the way you define categories of the target variable. With an ordinal variable, the important thing is the ordering of categories. So, if you collapse two adjacent categories into one larger category, you are making only a small change, and models built using the old and new categorizations should be very similar. Unfortunately, because linear regression is sensitive to the categorization used, a model built before merging categories could be quite different from one built after. Below are some examples pf ordered logistic regression: Example 1: A marketing research firm wants to investigate what factors influence the size of soda (small, medium, large or extra large) that people order at a fast-food chain. These factors may include what type of sandwich is ordered (burger or chicken), whether or not fries are also ordered, and age of the consumer. While the outcome variable, size of soda, is obviously ordered, the difference between the various sizes is not consistent. The difference between small and medium is 10 ounces, between medium and large 8, and between large and extra large 12. Example 2: A researcher is interested in what factors influence modaling in Olympic swimming. Relevant predictors include at training hours, diet, age, and popularity of swimming in the athlete’s home country. The researcher believes that the distance between gold and silver is larger than the distance between silver and bronze. Example 3: A study looks at factors that influence the decision of whether to apply to graduate school. College juniors are asked if they are unlikely, somewhat likely, or very likely to apply to graduate school. Hence, our outcome variable has three categories. Data on parental educational status, whether the undergraduate institution is public or private, and current GPA is also collected. The researchers have reason to believe that the “distances” between these three points are not equal. For example, the “distance” between “unlikely” and “somewhat likely” may be shorter than the distance between “somewhat likely” and “very likely”. How to use and get result by Ordinal Regression: Clink this link for PDF                                                                                                                                                                                                                                                                                                                        PDF source: http://www.norusis.com

Persistent properties are stored in ThingWorx database while non-persistent properties are stored in memory. This means that the persistent values do not get erased or deleted if the thing restarts or platform restarts. The persistent properties can also be retrieved in the same way as non-persistent properties. To explain better how we can retrieve persistent data, please consider the below example: I took a device group which has multiple devices and defined 2 persistent properties. One is serial number and the other is firmware version number. Now in a mashup builder, the user gives the serial number and the corresponding firmware version will be retrieved. I achieved this by writing services for that thing. Created a Data shape with the name DeviceData and defined two filed definitions. One is Serial number and the other is firmware version. Created a thing template with the name DevGroup. Added a property in the thing template with the name DeviceData and selected the basetype as info table. Also selected the previously created data shape (Device Data) in data shape field. And made this property as persistent data by selecting Is Persistent. Saved the property. Created two devices with the names Device1 and Device2. Both the devices use the above template. Now for each device set the property values by clicking the set button in properties link. These values will be persistent, meaning they do not change even after refresh or when you restart ThingWorx. You can even set these properties at run time by just creating a service. Created GetFirmversion service which retrieves the firm version of given service number. Created mashup as shown below: . Once you select Device1, enter the serial number in the numeric entry field and click query button, the firmware version of the given serial number is displayed along with the data of device1. If we enter a wrong number, no data will be displayed. You can also set an error message instead of displaying empty values. Similar case when we select the device2 data. This is one way to retrieve persistent data. You can also obtain the same in many ways.

The AddStreamEntries​ snippet does not offer too much information, except that it needs an InfoTable as input. It is however based on the InfoTable for the AddStreamEntity service.     To use the AddStreamEntries table, an InfoTable based on sourceType, values, location, source, timestamp​ and ​tags​ must be used.   In this example, I started with a new Thing based on a ​Stream​ template and the following DataShape:     This DataShape must be converted into an InfoTable with is used as the ​values​ parameter. It's important that the ​timestamp​ parameter has distinct values! Otherwise values matching the same timestamp will be overwritten!   We don't really need the sourceType​ as ThingWorx will automatically determine the type by knowing the source and which kind of Entity Type it is.   I created a new ​MyStreamThing​ with a new service, filling the InfoTable and the Stream. The result is the following code which will add 5 rows to the Stream:     // *** SET UP META DATA FOR INFO TABLE ***   // create a new InfoTable based on AddStreamEntries parameters (timestamp, location, source, sourceType, tags, values)   var myInfoTable = { dataShape: { fieldDefinitions : {} }, rows: [] };   myInfoTable.dataShape.fieldDefinitions['timestamp']  = { name: 'timestamp', baseType: 'DATETIME' }; myInfoTable.dataShape.fieldDefinitions['location']  = { name: 'location', baseType: 'LOCATION' }; myInfoTable.dataShape.fieldDefinitions['source']    = { name: 'source', baseType: 'STRING' }; myInfoTable.dataShape.fieldDefinitions['sourceType'] = { name: 'sourceType', baseType: 'STRING' }; myInfoTable.dataShape.fieldDefinitions['tags']      = { name: 'tags', baseType: 'TAGS' }; myInfoTable.dataShape.fieldDefinitions['values']    = { name: 'values', baseType: 'INFOTABLE' };   // *** SET UP ACTUAL VALUES FOR INFO TABLE ***   // create new meta data   var tags = new Array(); var timestamp = new Date(); var location = new Object(); location.latitude = 0; location.longitude = 0; location.elevation = 0; location.units = "WGS84";   // add rows to InfoTable (~5 times)   for (i=0; i<5; i++) {       // create new values based on Stream DataShape       var params = {           infoTableName : "InfoTable",           dataShapeName : "Cxx-DS"     };       var values = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape(params);       // add something to the values to make them unique       // create and add new row based on Stream DataShape     // only a single line allowed!       var newValues = new Object();     newValues.a = "aaa" + i; // STRING - isPrimaryKey = true     newValues.b = "bbb" + i; // STRING     newValues.c = "ccc" + i; // STRING       values.AddRow(newValues);       // create new InfoTable row based on meta data & values     // add 10 ms to each object, to make it's timestamp unique     // otherwise entries with the same timestamp will be overwritten       var newEntry = new Object();     newEntry.timestamp = new Date(Date.now() + (i * 10));     newEntry.location = location;     newEntry.source = me.name;     newEntry.tags = tags;     newEntry.values = values;       // add new Info Table row to Info Table           myInfoTable.rows = newEntry;       }       // *** ADD myInfoTable (HOLDING MULITPLE STREAM ENTRIES) TO STREAM       // add stream entries in the InfoTable       var params = {           values: myInfoTable /* INFOTABLE */     };       // no return       Things["MyStreamThing"].AddStreamEntries(params);   To verify the values have been added correctly, call the ​GetStreamEntriesWithData​ service on the ​MyStreamThing​

It usually happens that we need to copy a large file to ThingWorx server periodically, and what's worse, the big file is changing(like a log file). This sample give a simpler way to implement. The main idea in the sample is: 1. Lower the management burden from ThingWorx server and instead it put all the work in edge SDK side 2. Save network burden with only uploading the incremented file and append it to the older file on ThingWorx server   Java SDK version in this sample: 6.0.1-255

This simple example creates an infotable of hierarchical data from an existing datashape that can be used in a tree or D3 Tree widget.  Attachments are provides that give a PDF document as well as the ThingWorx entities for the example.  If you were just interested in the service code, it is listed below: var params = {   infoTableName : "InfoTable",   dataShapeName : "TreeDataShape" }; // CreateInfoTableFromDataShape(infoTableName:STRING("InfoTable"), dataShapeName:STRING):INFOTABLE(TreeDataShape) var result = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape(params); var NewRow = {}; NewRow.Parent = "No Parent"; NewRow.Child = "Enterprise"; NewRow.ChildDescription = "Enterprise"; result.AddRow(NewRow); NewRow.Parent = "Enterprise"; NewRow.Child = "Site1"; NewRow.ChildDescription = "Wilmington Plant"; result.AddRow(NewRow); NewRow.Parent = "Site1"; NewRow.Child = "Site1.Line1"; NewRow.ChildDescription = "Blow Molding"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line1"; NewRow.Child = "Site1.Line1.Asset1"; NewRow.ChildDescription = "Preform Staging"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line1"; NewRow.Child = "Site1.Line1.Asset2"; NewRow.ChildDescription = "Blow Molder"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line1"; NewRow.Child = "Site1.Line1.Asset3"; NewRow.ChildDescription = "Bottle Unscrambler"; result.AddRow(NewRow); NewRow.Parent = "Site1"; NewRow.Child = "Site1.Line2"; NewRow.ChildDescription = "Filling"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line2"; NewRow.Child = "Site1.Line2.Asset1"; NewRow.ChildDescription = "Rinser"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line2"; NewRow.Child = "Site1.Line2.Asset2"; NewRow.ChildDescription = "Filler"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line2"; NewRow.Child = "Site1.Line2.Asset3"; NewRow.ChildDescription = "Capper"; result.AddRow(NewRow); NewRow.Parent = "Site1"; NewRow.Child = "Site1.Line3"; NewRow.ChildDescription = "Packaging"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line3"; NewRow.Child = "Site1.Line3.Asset1"; NewRow.ChildDescription = "Printer Labeler"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line3"; NewRow.Child = "Site1.Line3.Asset2"; NewRow.ChildDescription = "Packer"; result.AddRow(NewRow); NewRow.Parent = "Site1.Line3"; NewRow.Child = "Site1.Line3.Asset3"; NewRow.ChildDescription = "Palletizing"; result.AddRow(NewRow); NewRow.Parent = "Enterprise"; NewRow.Child = "Site2"; NewRow.ChildDescription = "Mobile Plant"; result.AddRow(NewRow); NewRow.Parent = "Site2"; NewRow.Child = "Site2.Line1"; NewRow.ChildDescription = "Blow Molding"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line1"; NewRow.Child = "Site2.Line1.Asset1"; NewRow.ChildDescription = "Preform Staging"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line1"; NewRow.Child = "Site2.Line1.Asset2"; NewRow.ChildDescription = "Blow Molder"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line1"; NewRow.Child = "Site2.Line1.Asset3"; NewRow.ChildDescription = "Bottle Unscrambler"; result.AddRow(NewRow); NewRow.Parent = "Site2"; NewRow.Child = "Site2.Line2"; NewRow.ChildDescription = "Filling"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line2"; NewRow.Child = "Site2.Line2.Asset1"; NewRow.ChildDescription = "Rinser"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line2"; NewRow.Child = "Site2.Line2.Asset2"; NewRow.ChildDescription = "Filler"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line2"; NewRow.Child = "Site2.Line2.Asset3"; NewRow.ChildDescription = "Capper"; result.AddRow(NewRow); NewRow.Parent = "Site2"; NewRow.Child = "Site2.Line3"; NewRow.ChildDescription = "Packaging"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line3"; NewRow.Child = "Site2.Line3.Asset1"; NewRow.ChildDescription = "Printer Labeler"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line3"; NewRow.Child = "Site2.Line3.Asset2"; NewRow.ChildDescription = "Packer"; result.AddRow(NewRow); NewRow.Parent = "Site2.Line3"; NewRow.Child = "Site2.Line3.Asset3"; NewRow.ChildDescription = "Palletizing"; result.AddRow(NewRow);

This Javascript snippet creates a random value between those limits:   var dbl_Value = Math.floor(Math.random()*(max-min+1)+min);

First of all wishing everyone a blessed 2017 So here is a little something that hopefully can be helpful for all you Thingworx developers! This is a 'Remote Monitoring Application Starter' Mainly this is created around Best Practices for Security and provides a lot of powerful Modeling and Mashup techniques. Also has some cool Dashboard techniques Everything is documented in accompanying documents also in the zip (sorry went through a few steps to get this up properly. Install instructions: Thingworx Remote Monitoring Starter Application – Installation Guide Files All files needed are in a Folder called: RemoteMonitoringStarter, this is an Export to ThingworxStorage Extensions Not included, but the application uses the GoogleWidgetsExtension (Google Map) Steps Import Google Map extension. Place RemoteMonitoringStarter folder in the ThingworxStorage exports folder. From Thingworx do an Import from ThingworxStorage – Include Data, Use Default Persistence Provider, do NOT ignore Subsystems. After the import has finished, go to Organizations and open Everyone. In the Organization remove Users from the Everyone organization unit. Go to DataTables and open PTC.RemoteMonitoring.Simulation.DT Go to Services and execute SetSimulationValues Go to the UserManagementSubsystem In the Configuration section add PTC.RemoteMonitoring.Session.TS to the Session. Note: This step may already be done. Note: Screen shots provided at the end. Account Passwords FullAdmin/FullAdmin All other users have a password of: password. NOTE: You may have to Reset your Administrator password using the FullAdmin account. I also recommend changing the passwords after installing.

This example shows how a file can be retrieved via Scripto and then displayed on a Web page. Precondition is that an asset has an uploaded file. This script assumes the file is there and that it is not extremely large (under 1 megabyte). This example uses base64 encoding to convert the file into a string. Future versions of Scripto will support other data streams so that base64 encoding will not be necessary. import com.axeda.drm.sdk.Context import com.axeda.drm.sdk.data.UploadedFile import com.axeda.drm.sdk.data.UploadedFileFinder import com.axeda.drm.sdk.device.Device import com.axeda.drm.sdk.device.DeviceFinder // This script requires parameter "id" Context ctx = Context.create(parameters.username); def response = '' try {     DeviceFinder deviceFinder = new DeviceFinder(ctx, new Identifier(parameters.id as Integer));     Device device = deviceFinder.find();     UploadedFileFinder uff = new UploadedFileFinder(ctx)     uff.device = device     uff.hint = 'photo'     def ufiles = uff.findAll()     UploadedFile ufile     if (ufiles.size() > 0) {         ufile = ufiles[0]         File f = ufile.extractFile()         response = getBytes(f).encodeBase64(false).toString()     } } catch (Exception e) {     logger.info(e.message);     response = [             faultcode: 'Groovy Exception',             faultstring: e.message     ]; } return ['Content-Type': 'data:image/png;base64', 'Content': response]; static byte[] getBytes(File file) throws IOException {     return getBytes(new FileInputStream(file)); } static byte[] getBytes(InputStream is) throws IOException {     ByteArrayOutputStream answer = new ByteArrayOutputStream(); // reading the content of the file within a byte buffer     byte[] byteBuffer = new byte[8192];     int nbByteRead /* = 0*/;     try {         while ((nbByteRead = is.read(byteBuffer)) != -1) { // appends buffer             answer.write(byteBuffer, 0, nbByteRead);         }     } finally {         is.close()     }     return answer.toByteArray(); }

This project is a simple custom tab that allows you to search all models and see their assets with basic information.  It is packaged as an Axeda SDK v2 Artisan project. Further Reading Developing with Axeda Artisan (Axeda Platform v6.8 and later) Axeda Sample Application: Populating A Web Page with Data Items Extending the Axeda Platform UI - Custom Tabs and Modules

The following script takes a parameter of a model name, a device serial number and a data item name, finds the asset location and uses that longitude to determine the current TimeZone.  It then converts the Timezone of the data item timestamp to an Eastern Standard Timezone timestamp. import groovy.xml.MarkupBuilder import com.axeda.drm.sdk.Context import java.util.TimeZone import com.axeda.drm.sdk.data.* import com.axeda.drm.sdk.device.* import com.axeda.common.sdk.jdbc.*; import net.sf.json.JSONObject import net.sf.json.JSONArray import com.axeda.drm.sdk.mobilelocation.MobileLocationFinder import com.axeda.drm.sdk.mobilelocation.MobileLocation import com.axeda.drm.sdk.mobilelocation.CurrentMobileLocationFinder def response try {     Context ctx = Context.getUserContext()     ModelFinder mfinder = new ModelFinder(ctx)     mfinder.setName(parameters.model_name)     Model m = mfinder.find()     DeviceFinder dfinder = new DeviceFinder(ctx)     dfinder.setModel(m);     dfinder.setSerialNumber(parameters.device)     Device d = dfinder.find()     CurrentMobileLocationFinder cmlFinder = new CurrentMobileLocationFinder(ctx);     cmlFinder.setDeviceId(d.id.getValue());     MobileLocation ml = cmlFinder.find();     def lng = -72.158203125     if (ml?.lng){         lng = ml?.lng     }     // set boundaries for timezones - longitudes     def est = setUSTimeZone(-157.95415000000003)     def tz = setUSTimeZone(lng)     CurrentDataFinder cdfinder = new CurrentDataFinder(ctx, d)     DataValue dvalue = cdfinder.find(parameters.data_item_name)     def adjtime = convertToNewTimeZone(dvalue.getTimestamp(),tz,est)     def results = JSONObject.fromObject(lat: ml?.lat, lng: ml?.lng, current: [name: dvalue.dataItem.name, time: adjtime.format("MM/dd/yyyy HH:mm"), value: dvalue.asString()]).toString(2)     response = results } catch (Exception e) {     response = [                 message: "Error: " + e.message             ]     response =  JSONObject.fromObject(response).toString(2) } return ['Content-Type': 'application/json', 'Cache-Control':'no-cache', 'Content': response] def setUSTimeZone(lng){     TimeZone tz     // set boundaries for US timezones by longitude     if (lng <= -67.1484375 && lng > -85.517578125){         tz = TimeZone.getTimeZone("EST");     }     else if (lng <= -85.517578125 && lng > -96.591796875){         tz = TimeZone.getTimeZone("CST");     }     else if (lng <= -96.591796875 && lng > -113.90625){         tz = TimeZone.getTimeZone("MST");     }     else if (lng <= -113.90625){         tz = TimeZone.getTimeZone("PST");     }     logger.info(tz)     return tz } public Date convertToNewTimeZone(Date date, TimeZone oldTimeZone, TimeZone newTimeZone){     long oldDateinMilliSeconds=date.time - oldTimeZone.rawOffset     // oldtimeZone.rawOffset returns the difference(in milliSeconds) of time in that timezone with the time in GMT     // date.time returns the milliseconds of the date     Date dateInGMT=new Date(oldDateinMilliSeconds)     long convertedDateInMilliSeconds = dateInGMT.time + newTimeZone.rawOffset     Date convertedDate = new Date(convertedDateInMilliSeconds)     return convertedDate }

For a recent project, I was needing to find all of the children in a Network Hierarchy of a particular template type... so I put together a little script that I thought I'd share. Maybe this will be useful to others as well.   In my situation, this script lived in the Location template. This was useful so that I could find all the Sensor Things under any particular node, no matter how deep they are.   For example, given a network like this: Location 1 Sensor 1 Location 1A Sensor 2 Sensor 3 Location 1AA Sensor 4 Location 1B Sensor 5 If you run this service in Location 1, you'll get an InfoTable with these Things: Sensor 1 Sensor 2 Sensor 3 Sensor 4 Sensor 5 From Location 1A: Sensor 2 Sensor 3 Sensor 4 From Location 1AA: Sensor 4 From Location 1B: Sensor 5   For this service, these are the inputs/outputs: Inputs: none Output: InfoTable of type NetworkConnection   // CreateInfoTableFromDataShape(infoTableName:STRING("InfoTable"), dataShapeName:STRING):INFOTABLE(AlertSummary) let result = Resources["InfoTableFunctions"].CreateInfoTableFromDataShape({ infoTableName : "InfoTable", dataShapeName : "NetworkConnection" }); // since the hierarchy could contain locations or sensors, need to recursively loop down to get all the sensors function findChildrenSensors(thingName) { let childrenThings = Networks["Hierarchy_NW"].GetChildConnections({ name: thingName /* STRING */ }); for each (var row in childrenThings.rows) { // row.to has the name of the child Thing if (Things[row.to].IsDerivedFromTemplate({thingTemplateName: "Location_TT"})) { findChildrenSensors(row.to); } else if (Things[row.to].IsDerivedFromTemplate({thingTemplateName: "Sensor_TT"})) { result.AddRow(row); } } } findChildrenSensors(me.name);    

ThingWorx is great for storing large amounts of data coming from your devices but it can also be used like a traditional, row based database for information you would like to integrate with your thing data. Attached to this blog entry is a short example of creating an address book database using a DataTable and a DataShape. It does not focus on creating mashups but sticks with discussing the modeling and service calls you would use to create a simple database.

Here is a spreadsheet that I created which helps to estimate data transfer volumes for the purpose of estimating egress costs when transferring data out of region.   You find that there are a number of input parameters like numbers of assets, properties, file sizes, compression ratio, as well as a page with the cost elements which can be updated from the Interweb.    

Axeda Machine Streams enables external Platform integrators to access the current, raw data from connected assets. The Platform can stream the data item, alarm, mobile location, and registration messages from connected assets to an ActiveMQ server or Azure Service Bus endpoint. Streamed data can be used for data analytics or reporting, or simply for storage. This article explains the Machine Streams Data Relay project that Axeda provides. This sample project illustrates how stream consumers can create their own projects to relay Machine Stream messages from ActiveMQ or Azure Service Bus into their environments. The Machine Streams Data Relay project was created using Apache Maven. The project operates by dispatching messages to a log message processor. Each machine streams message is logged to stdout. Note: The "Axeda Features Guide" provides a high level introduction to the Axeda Machine Streams feature. That PDF is available from PTC Support (http://support.ptc.com/).) Downloading and Installing the Project The machine-streams-data-relay project is provided as a tar.gz archive for Linux users and a .zip archive for Windows users. Each archive includes a Maven project with all source code. This page provides downloads and full source for the machine-steams-data-relay Maven project. The Data Relay project files are available from here. Prerequisites To download, build, and compile the machine-streams-data-relay project, you will need the following: Access to an Axeda Platform instance configured to stream asset data (for ActiveMQ endpoint this includes the Axeda provided ActiveMQ machine-streams plugin/overlay (axeda-jms-plugin-r<SVN_REVISION>-machine-streams.zip, which is provided here. ActiveMQ or Azure Service Bus server configured for Machine Streams. Instructions for configuring an ActiveMQ or Azure Service Bus server for Machine Streams are provided in the “Axeda® Machine Streams: A Guide to Setting Up Broker Endpoints", available with all documentation from PTC Support (http://support.ptc.com/). At least one machine stream (Axeda Artisan Machine Streams Archetype) configured to stream data to the ActiveMQ or Azure Service Bus server for your assets. (Complete information about creating machine streams and adding machine stream support to the Axeda Platform is provided in the “Axeda v2 API/Services Developers Reference Guide” available from PTC Support (http://support.ptc.com/).) Access to the ActiveMQ or Azure Service Bus server configured as the endpoint for streamed Machine Streams content Oracle Java JDK 1.7 or greater and java and javac installed and available in your PATH (if you need instructions for this, see http://www.oracle.com/technetwork/java/javase/downloads/index.html) Maven 3.0.4 or greater and mvn installed and available in your PATH (if you need instructions for this, see http://maven.apache.org/download.cgi) Note: For the Machine Streams Data Relay project to work successfully, the Axeda Platform instance and the ActiveMQ or Azure Service Bus server instance must be configured with support for Axeda Machine Streams, and at least one machine stream must be configured to stream data. Complete information about configuring Axeda Platform for Axeda Machine Streams, including the data format for the resulting streams (XML or JSON) is available in the “Axeda v2 API/Services Developers Reference Guide.” Instructions for configuring an ActiveMQ or Azure Service Bus server for Machine Streams are provided in the “Axeda® Machine Streams: A Guide to Setting Up Broker Endpoints” Reference Guide (available from PTC Support (http://support.ptc.com/)). Building the Project This page provides instructions for building the Data Relay project for Linux and for Windows environments. 1. Download and uncompress the project for your environment Linux: Click here for the machine-streams-data-relay-1.0.3-project.tar.gz # tar -zxvf machine-streams-data-relay-1.0.3-project.tar.gz # cd machine-streams-data-relay-1.0.3 Windows: Click here for the machine-streams-data-relay-1.0.3-project.zip Unzip the project to the following directory: C:\machine-streams-data-relay-1.0.3 2. Edit the ActiveMQ or Azure Service Bus configuration file (configAMQ.properties or configASB.properties) in src\main\scripts\ as needed. sample Config.properties files for the MachineStreamsDataRelay component For ActiveMQ broker endpoints - configAMQ.properties # The ActiveMQ broker URL. brokerURL=tcp://localhost:62000 # The ActiveMQ queue name to process messages from. # It can be a single queue: MachineStream.stream01 # Or a wildcard queue: MachineStream.> queueName=MachineStream.> # The username used to connect to the ActiveMQ queue username=axedaadmin # The password used to connect to the ActiveMQ queue password=zQXuLzhQgcyRZ25JCDXYEPBCT2kx48 # The number of ActiveMQ broker connections. numConnections=10 # The number of sessions per connection. Note that each session will create a separate thread. numSessionsPerConnection=5 # The number of concurrent threads used for processing machine streams messages. numProcessingThreads=100 # The type of message listener container. # default = single queue name per connection. # multiDestination = supports multiple queue names per connection messageListenerContainerType=default For Azure Service Bus broker endpoints - configASB.properties # The ASB broker URL. brokerURL=amqps://your-azure-service-bus-namespace.servicebus.windows.net # The ASB queues to process messages from. # It can be a single queue: MachineStream.stream01 # Or multiple queues separated by a comma: MachineStream.stream01,MachineStream.stream02 # Or a queue range defined by the following syntax: MachineStream.stream[01-20] queueName=MachineStream.stream[01-50] # The username used to connect to the ASB queue(s) username=your-azure-service-bus-username # The password used to connect to the ASB queue(s) password=the-password-for-your-azure-service-bus-username # The max number of ASB broker connections. numConnections=10 # The number of concurrent threads used for processing machine streams messages. numProcessingThreads=100 # The type of message listener container. # default = single queue name per connection. # multiDestination = supports multiple queue names per connection messageListenerContainerType=multiDestination Note: messageListenerContainerType is provided because Azure Service Bus does not support wildcard queue names. The configuration details are as follows: Name Description Value brokerURL location of the ActiveMQ or Azure Service Bus (broker) location of the ActiveMQ or Azure Service Bus server (broker) queueName Name of the ActiveMQ or Azure Service Bus queue from which you want to process messages To define a single queue: MachineStream.<insert single queue name here> To define a wildcard queue name for multiple queues:MachineStream. It can be a single queue:  MachineStream.stream01 Or multiple queues separated by a comma: MachineStream.stream01,MachineStream.stream02 Or a queue range defined by the following syntax: MachineStream.stream[01-20]: queueName=MachineStream.stream[01-50] (if you have multiple queues and you want to use ASB, then you have to use multiDestination and use the range) username username used to connect to the ActiveMQ or Azure Service Bus queue For ActiveMQ: username=axedaadmin For ASB: username=your-azure-service-bus-username password used to connect to the ActiveMQ or Azure Service Bus queue password used to connect to the ActiveMQ or ASB queue(s) numConnections number of ActiveMQ or Azure Service Bus broker connections Default is 10 broker connections numSessionsPerConnection The number of sessions per connection. Note that each session will create a separate thread. (This key is used infrequently.) APPLICABLE TO ACTIVEMQ ONLY. Default is 5 sessions per connection APPLICABLE TO ACTIVEMQ ONLY. numProcessingThreads The number of concurrent threads used for processing machine streams messages. Default is 100 concurrent threads messageListenerContainerType The type of message listener container. Default is single queue name per connection. Supports multiple queue names per connection 3. Build code using Maven.  Use -DskipTests option if you want to skip tests.  This will build all source code and produce a bin archive in the target directory. For Linux: # mvn package -DskipTests For Windows: c:\> mvn package -DskipTests 4. Enter the target directory and uncompress *bin.tar.gz archive and enter correct directory For Linux: # cd target # tar -zxvf machine-streams-data-relay-1.0.3-bin.tar.gz # cd machine-streams-data-relay-1.0.3 For Windows: c:\> cd target c:\> unzip machine-streams-data-relay-1.0.3.bin.zip c:\> cd machine-streams-data-relay-1.0.3 5. Start the application. For Linux: # ./machineStreamsDataRelay.sh <config properties file> for example: e.g. ./machineStreamsDataRelay.sh configOfYourChoice.properties For Windows: # ./machineStreamsDataRelay.sh <config properties file> for example: e.g. ./machineStreamDataRelay.bat configOfYourChoice.properties See the two example config files included within the project: configASB.properties (for Azure Service Bus) and configAMQ.properties (for ActiveMQ). 6. Scan the output. If your ActiveMQ configuration is correct, output similar to the following should appear, and no ERRORS should be shown: 2014-03-26 10:27:06.179 [main] INFO  [MessageListenerServiceImpl]: Initializing connections to tcp://localhost:62000 username=axedaadmin 2014-03-26 10:27:06.346 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 1: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.351 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 2: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.356 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 3: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.365 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 4: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.369 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 5: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.381 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 6: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.388 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 7: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.402 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 8: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.411 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 9: queue=MachineStream.> numSessions=5 2014-03-26 10:27:06.416 [main] INFO  [MessageListenerServiceImpl]: Initialized connection 10: queue=MachineStream.> numSessions=5 If your Azure Service Bus configuration is correct, output similar to the following should appear, and no ERRORS should be shown: 2014-10-01 16:51:30.114 [main] INFO [MessageListenerServiceImpl]: Initializing Connections to amqps://acme.servicebus.windows.net username=owner 2014-10-01 16:51:31.613 [ConnectionRecovery-thread-6] INFO [MultiDestinationMessageListenerContainer]: Connection 6 created 0/10 queue consumers 2014-10-01 16:51:31.614 [ConnectionRecovery-thread-8] INFO [MultiDestinationMessageListenerContainer]: Connection 8 created 0/10 queue consumers 2014-10-01 16:51:31.614 [ConnectionRecovery-thread-10] INFO [MultiDestinationMessageListenerContainer]: Connection 10 created 0/9 queue consumers 2014-10-01 16:51:31.614 [ConnectionRecovery-thread-2] INFO [MultiDestinationMessageListenerContainer]: Connection 2 created 0/10 queue consumers 2014-10-01 16:51:31.614 [ConnectionRecovery-thread-3] INFO [MultiDestinationMessageListenerContainer]: Connection 3 created 0/10 queue consumers 2014-10-01 16:51:31.614 [ConnectionRecovery-thread-5] INFO [MultiDestinationMessageListenerContainer]: Connection 5 created 0/10 queue consumers 2014-10-01 16:51:31.615 [ConnectionRecovery-thread-9] INFO [MultiDestinationMessageListenerContainer]: Connection 9 created 0/10 queue consumers 2014-10-01 16:51:31.615 [ConnectionRecovery-thread-4] INFO [MultiDestinationMessageListenerContainer]: Connection 4 created 0/10 queue consumers 2014-10-01 16:51:31.621 [ConnectionRecovery-thread-7] INFO [MultiDestinationMessageListenerContainer]: Connection 7 created 0/10 queue consumers 2014-10-01 16:51:31.756 [ConnectionRecovery-thread-1] INFO [MultiDestinationMessageListenerContainer]: Connection 1 created 0/10 queue consumers 2014-10-01 16:51:32.613 [ConnectionRecovery-thread-6] INFO [MultiDestinationMessageListenerContainer]: Connection 6 created 9/10 queue consumers 2014-10-01 16:51:32.614 [ConnectionRecovery-thread-8] INFO [MultiDestinationMessageListenerContainer]: Connection 8 created 9/10 queue consumers 2014-10-01 16:51:32.614 [ConnectionRecovery-thread-10] INFO [MultiDestinationMessageListenerContainer]: Connection 10 created 7/9 queue consumers 2014-10-01 16:51:32.614 [ConnectionRecovery-thread-2] INFO [MultiDestinationMessageListenerContainer]: Connection 2 created 10/10 queue consumers 2014-10-01 16:51:32.615 [ConnectionRecovery-thread-3] INFO [MultiDestinationMessageListenerContainer]: Connection 3 created 9/10 queue consumers 2014-10-01 16:51:32.615 [ConnectionRecovery-thread-5] INFO [MultiDestinationMessageListenerContainer]: Connection 5 created 0/10 queue consumers 2014-10-01 16:51:32.615 [ConnectionRecovery-thread-9] INFO [MultiDestinationMessageListenerContainer]: Connection 9 created 7/10 queue consumers 2014-10-01 16:51:32.615 [ConnectionRecovery-thread-4] INFO [MultiDestinationMessageListenerContainer]: Connection 4 created 9/10 queue consumers 2014-10-01 16:51:32.623 [ConnectionRecovery-thread-7] INFO [MultiDestinationMessageListenerContainer]: Connection 7 created 9/10 queue consumers 2014-10-01 16:51:32.756 [ConnectionRecovery-thread-1] INFO [MultiDestinationMessageListenerContainer]: Connection 1 created 10/10 queue consumers 2014-10-01 16:51:32.833 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 1: numQueues=10 initTimeMillis=2631 millis 2014-10-01 16:51:32.833 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 2: numQueues=10 initTimeMillis=2488 millis 2014-10-01 16:51:33.613 [ConnectionRecovery-thread-6] INFO [MultiDestinationMessageListenerContainer]: Connection 6 created 10/10 queue consumers 2014-10-01 16:51:33.614 [ConnectionRecovery-thread-8] INFO [MultiDestinationMessageListenerContainer]: Connection 8 created 10/10 queue consumers 2014-10-01 16:51:33.614 [ConnectionRecovery-thread-10] INFO [MultiDestinationMessageListenerContainer]: Connection 10 created 9/9 queue consumers 2014-10-01 16:51:33.615 [ConnectionRecovery-thread-3] INFO [MultiDestinationMessageListenerContainer]: Connection 3 created 9/10 queue consumers 2014-10-01 16:51:33.615 [ConnectionRecovery-thread-5] INFO [MultiDestinationMessageListenerContainer]: Connection 5 created 0/10 queue consumers 2014-10-01 16:51:33.615 [ConnectionRecovery-thread-9] INFO [MultiDestinationMessageListenerContainer]: Connection 9 created 8/10 queue consumers 2014-10-01 16:51:33.615 [ConnectionRecovery-thread-4] INFO [MultiDestinationMessageListenerContainer]: Connection 4 created 9/10 queue consumers 2014-10-01 16:51:33.623 [ConnectionRecovery-thread-7] INFO [MultiDestinationMessageListenerContainer]: Connection 7 created 10/10 queue consumers 2014-10-01 16:51:34.615 [ConnectionRecovery-thread-5] INFO [MultiDestinationMessageListenerContainer]: Connection 5 created 0/10 queue consumers 2014-10-01 16:51:34.615 [ConnectionRecovery-thread-3] INFO [MultiDestinationMessageListenerContainer]: Connection 3 created 9/10 queue consumers 2014-10-01 16:51:34.615 [ConnectionRecovery-thread-9] INFO [MultiDestinationMessageListenerContainer]: Connection 9 created 8/10 queue consumers 2014-10-01 16:51:34.615 [ConnectionRecovery-thread-4] INFO [MultiDestinationMessageListenerContainer]: Connection 4 created 9/10 queue consumers 2014-10-01 16:51:35.615 [ConnectionRecovery-thread-5] INFO [MultiDestinationMessageListenerContainer]: Connection 5 created 9/10 queue consumers 2014-10-01 16:51:35.615 [ConnectionRecovery-thread-3] INFO [MultiDestinationMessageListenerContainer]: Connection 3 created 9/10 queue consumers 2014-10-01 16:51:35.615 [ConnectionRecovery-thread-9] INFO [MultiDestinationMessageListenerContainer]: Connection 9 created 8/10 queue consumers 2014-10-01 16:51:35.616 [ConnectionRecovery-thread-4] INFO [MultiDestinationMessageListenerContainer]: Connection 4 created 9/10 queue consumers 2014-10-01 16:51:36.616 [ConnectionRecovery-thread-5] INFO [MultiDestinationMessageListenerContainer]: Connection 5 created 9/10 queue consumers 2014-10-01 16:51:36.616 [ConnectionRecovery-thread-3] INFO [MultiDestinationMessageListenerContainer]: Connection 3 created 9/10 queue consumers 2014-10-01 16:51:36.616 [ConnectionRecovery-thread-4] INFO [MultiDestinationMessageListenerContainer]: Connection 4 created 9/10 queue consumers 2014-10-01 16:51:36.616 [ConnectionRecovery-thread-9] INFO [MultiDestinationMessageListenerContainer]: Connection 9 created 8/10 queue consumers 2014-10-01 16:51:37.616 [ConnectionRecovery-thread-5] INFO [MultiDestinationMessageListenerContainer]: Connection 5 created 9/10 queue consumers 2014-10-01 16:51:37.616 [ConnectionRecovery-thread-3] INFO [MultiDestinationMessageListenerContainer]: Connection 3 created 9/10 queue consumers 2014-10-01 16:51:37.616 [ConnectionRecovery-thread-4] INFO [MultiDestinationMessageListenerContainer]: Connection 4 created 9/10 queue consumers 2014-10-01 16:51:37.616 [ConnectionRecovery-thread-9] INFO [MultiDestinationMessageListenerContainer]: Connection 9 created 8/10 queue consumers 2014-10-01 16:51:38.616 [ConnectionRecovery-thread-3] INFO [MultiDestinationMessageListenerContainer]: Connection 3 created 10/10 queue consumers 2014-10-01 16:51:38.617 [ConnectionRecovery-thread-9] INFO [MultiDestinationMessageListenerContainer]: Connection 9 created 10/10 queue consumers 2014-10-01 16:51:38.616 [ConnectionRecovery-thread-4] INFO [MultiDestinationMessageListenerContainer]: Connection 4 created 10/10 queue consumers 2014-10-01 16:51:38.616 [ConnectionRecovery-thread-5] INFO [MultiDestinationMessageListenerContainer]: Connection 5 created 10/10 queue consumers 2014-10-01 16:51:38.643 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 3: numQueues=10 initTimeMillis=8491 millis 2014-10-01 16:51:38.643 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 4: numQueues=10 initTimeMillis=8490 millis 2014-10-01 16:51:38.643 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 5: numQueues=10 initTimeMillis=8490 millis 2014-10-01 16:51:38.643 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 6: numQueues=10 initTimeMillis=3485 millis 2014-10-01 16:51:38.643 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 7: numQueues=10 initTimeMillis=3495 millis 2014-10-01 16:51:38.643 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 8: numQueues=10 initTimeMillis=3485 millis 2014-10-01 16:51:38.643 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 9: numQueues=10 initTimeMillis=8488 millis 2014-10-01 16:51:38.643 [main] INFO [MessageListenerServiceImpl]: Initialized Connection 10: numQueues=9 initTimeMillis=3485 millis 7. To verify that messages are being streamed properly from the Axeda Platform, send DataItems from your connected Assets. You should see messages similar to the following. (Remember that each Asset you are testing must have an associated Machine Stream.) 2014-03-26 10:45:16.309 [pool-1-thread-1] INFO  [LogMessageProcessor]: StreamedDataItem: Model,Asset1,799021d6-70a3-7c32-0000-00000000021d,false,Wed Mar 26 14:45:16 EDT 2014,temp,43,analog 2014-03-26 10:45:21.137 [pool-1-thread-2] INFO  [LogMessageProcessor]: StreamedDataItem: Model,Asset2,799021d6-70a3-7c32-0000-000000000225,false,Wed Mar 26 14:45:21 EDT 2014,temp,43,analog 2014-03-26 10:45:26.134 [pool-1-thread-3] INFO  [LogMessageProcessor]: StreamedDataItem: Model,Asset1,799021d6-70a3-7c32-0000-00000000022b,false,Wed Mar 26 14:45:26 EDT 2014,temp,44,analog 2014-03-26 10:45:31.135 [pool-1-thread-4] INFO  [LogMessageProcessor]: StreamedDataItem: Model,Asset2,799021d6-70a3-7c32-0000-000000000231,false,Wed Mar 26 14:45:31 EDT 2014,temp,44,analog 2014-03-26 10:45:36.142 [pool-1-thread-5] INFO  [LogMessageProcessor]: StreamedDataItem: Model,Asset1,799021d6-70a3-7c32-0000-000000000237,false,Wed Mar 26 14:45:36 EDT 2014,temp,45,analog 2014-03-26 10:45:41.146 [pool-1-thread-6] INFO  [LogMessageProcessor]: StreamedDataItem: Model,Asset2,799021d6-70a3-7c32-0000-00000000023d,false,Wed Mar 26 14:45:41 EDT 2014,temp,45,analog Configuring a CustomMessageProcessor By default, the project is configured to use a LogMessageProcessor that logs each streamed message it receives to standard out. The project takes a StreamedMessage in either an XML or JSON format (as configured in the MachineStream SDKv2 object) and decodes the message into a StreamedMessage Java object. LogMessageProcessor.java implements the MessageProcessor interface. Here is the MessageProcessor.java interface: MessageProcessor.java package com.axeda.tools.streams.processor; import com.axeda.tools.streams.model.StreamedMessage; /** * This class defines the message processor method callback that will called for message processing. * Note that this methods will be called by multiple threads concurrently. */ public interface MessageProcessor { /** * Process a machine stream message. Note that this method will be called by multiple threads concurrently.  * The number of concurrent processing threads is defined in MachineStreamsConfig.getNumProcessingThreads(). * If you add code here that significantly slows down message processing, then there is the potential that * MessageListenerService threads will also block.  When the MessageListenerService threads block, this means that * messages will start to backup in the ActiveMQ or ASB message queues. If you are processing a large number of messages, * then you may need to adjust your configuration parameters or optimize your processMessage() code. * @param message machine streams message to process */public void processMessage(StreamedMessage message);} An additional class named CustomMessageProcessor.java has been provided so that you can provide your own custom message processing logic: CustomMessageProcessor.java package com.axeda.tools.streams.processor; import org.springframework.stereotype.Component; import com.axeda.tools.streams.model.StreamedAlarm; import com.axeda.tools.streams.model.StreamedDataItemMessage; import com.axeda.tools.streams.model.StreamedMessage; import com.axeda.tools.streams.model.StreamedMobileLocation; import com.axeda.tools.streams.model.StreamedRegistrationMessage; /** * This class was provided for customers to implement their own message processing business * logic. To use this class, change the @Autowired messageProcessor qualifier in * MessageProcessingServiceImpl.java to @Qualifier("customMessageProcessor") */ @Component("customMessageProcessor") public class CustomMessageProcessor implements MessageProcessor { /** * (non-Javadoc) * @see com.axeda.tools.streams.processor.MessageProcessor#processMessage(com.axeda.tools.streams.model.StreamedMessage) * * Process a machine stream message. Note that this method will be called by multiple threads * concurrently. The number of concurrent processing threads is defined in * MachineStreamsConfig.getNumProcessingThreads(). * If you add code here that significantly slows down message processing, then there is the * potential that MessageListenerService threads will also block. When the MessageListenerService * threads block, this means that messages will start to backup in the ActiveMQ or Azure Service Bus message queues. If you * are processing a large number of messages, then you may need to adjust your configuration parameters * or optimize your processMessage() code. */ @SuppressWarnings("unused") @Override public void processMessage(StreamedMessage message) { if (message instanceof StreamedDataItemMessage) { StreamedDataItemMessage dataItem = (StreamedDataItemMessage) message; // add your business logic here } else if (message instanceof StreamedAlarm) { StreamedAlarm alarm = (StreamedAlarm) message; // add your business logic here } else if (message instanceof StreamedMobileLocation) { StreamedMobileLocation mobileLocation = (StreamedMobileLocation) message; // add your business logic here } else if (message instanceof StreamedRegistrationMessage) { StreamedRegistrationMessage registration = (StreamedRegistrationMessage)message; // add your business logic here } } } The Axeda Platform Machine Streams feature currently support 4 different message types: StreamedDataItemMessage StreamedAlarm StreamedMobileLocation StreamedRegistrationMessage For each of the different message types, you should add your message processing business logic.  You may want to write each message to your favorite NoSql database or to a flat file. Once you have completed your changes to the CustomObjectMessageProcessor, then you must make one change in the MessageProcessingServiceImpl.java class to use this Spring bean. Uncomment this line  // @Qualifier("customMessageProcessor") Comment this line @Qualifier("logMessageProcessor") The following code snppet shows what your changes should look like when you are finished: MessageProcessingServiceImpl.java @Component("messageProcessingService") public class MessageProcessingServiceImpl implements MessageProcessingService private static final Logger LOGGER = LoggerFactory.getLogger(MessageProcessingServiceImpl.class); @Autowired private MessageDecoder messageDecoder; @Autowired // If you want to use the CustomMessageProcessor instead of the default LogMessageProcessor then change this Qualifier to @Qualifier("customMessageProcessor") //@Qualifier("logMessageProcessor") private MessageProcessor messageProcessor; private ExecutorService executorService;

One of the signature features of the Axeda Platform is our alarm notification, signalling and auditing capabilities.   Our dashboard offers a simplified view into assets that are in an alarm state, and provides interaction between devices and operators.  For some customers the dashboard may be too extensive for their application needs.  The Axeda Platform from versions 6.6 onward provide a number of ways of interacting with Alarms to allow you to present this data to remote clients (Android, iOS, etc.) or to build extended business logic around alarm processing. If one were to create a remote management application for Android, for example, there are the REST APIs available to interact with Assets and Alarms.  For aggregate operations where network traffic and round-trip time can be a concern, we have our Scripto API also available that allows you to use the Custom Object functionality to deliver information on many different aggregating criteria, and allow developers to get the data needed to build the applications to solve their business requirements. Shown below is a REST API call you might make to retrieve all alarms between a certain time and date. POST:   https://INSTANCENAME/services/v2/rest/alarm/find <v2:AlarmCriteria xmlns:v2="http://www.axeda.com/services/v2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">    <v2:date xsi:type="v2:BetweenDateQuery">     <v2:start>2015-01-01T00:00:00.000Z</v2:start>     <v2:end>2015-01-31T23:59:59.000Z</v2:end>   </v2:date>   <v2:states/> </v2:AlarmCriteria>   In a custom object, this would like like the following: import static com.axeda.sdk.v2.dsl.Bridges.* import com.axeda.services.v2.* import com.axeda.sdk.v2.exception.* def q = new com.axeda.services.v2.BetweenDateQuery() q.start = new Date() q.end = new Date() ac = new AlarmCriteria(date:q) aresults = alarmBridge.find(ac)   Using the same API endpoint, here's how you would retrieve data by severity: <v2:AlarmCriteria xmlns:v2="http://www.axeda.com/services/v2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">    <v2:severity xsi:type="v2:GreaterThanEqualToNumericQuery">     <v2:value>900</v2:value>   </v2:severity>   <v2:states/> </v2:AlarmCriteria>   Or in a custom object: import static com.axeda.sdk.v2.dsl.Bridges.* import com.axeda.services.v2.* import com.axeda.sdk.v2.exception.*   def q = new com.axeda.services.v2.GreaterThanEqualToNumericQuery() q.value = 900 ac = new AlarmCriteria(severity:q) aresults = alarmBridge.find(ac)   Currently the Query Types do not map properly in JSON objects - use XML to perform these types of queries via the REST APIs. References: Axeda v2 API/Services Developer's Reference Guide 6.6 Axeda Platform Web Services Developer Reference v2 REST 6.6 Axeda v2 API/Services Developer's Reference Guide 6.8 Axeda Platform Web Services Developer Reference v2 REST 6.8

OpenDJ is a directory server which is also the base for WindchillDS. It can be used for centralized user management and ThingWorx can be configured to login with users from this Directory Service.   Before we start Pre-requisiste Docker on Ubuntu JKS keystore with a valid certificate JKS keystore is stored in /docker/certificates - on the machine that runs the Docker environments Certificate is generated with a Subject Alternative Name (SAN) extension for hostname, fully qualified hostname and IP address of the opendj (Docker) server Change the blue phrases to the correct passwords, machine names, etc. when following the instructions If possible, use a more secure password than "Password123456"... the one I use is really bad   Related Links https://hub.docker.com/r/openidentityplatform/opendj/ https://backstage.forgerock.com/docs/opendj/2.6/admin-guide/#chap-change-certs https://backstage.forgerock.com/knowledge/kb/article/a43576900   Configuration Generate the PKCS12 certificate Assume this is our working directory on the Docker machine (with the JKS certificate in it)   cd /docker/certificates   Create .pin file containing the keystore password   echo "Password123456" > keystore.pin   Convert existing JKS keystore into a new PKCS12 keystore   keytool -importkeystore -srcalias muc-twx-docker -destalias server-cert -srckeystore muc-twx-docker.jks -srcstoretype JKS -srcstorepass `cat keystore.pin` -destkeystore keystore -deststoretype PKCS12 -deststorepass `cat keystore.pin` -destkeypass `cat keystore.pin`   Export keystore and Import into truststore   keytool -export -alias server-cert -keystore keystore -storepass `cat keystore.pin` -file server-cert.crt keytool -import -alias server-cert -keystore truststore -storepass `cat keystore.pin` -file server-cert.crt     Docker Image & Container Download and run   sudo docker pull openidentityplatform/opendj sudo docker run -d --name opendj --restart=always -p 389:1389 -p 636:1636 -p 4444:4444 -e BASE_DN=o=opendj -e ROOT_USER_DN=cn=Manager -e ROOT_PASSWORD=Password123456 -e SECRET_VOLUME=/var/secrets/opendj -v /docker/certificates:/var/secrets/opendj:ro openidentityplatform/opendj   After building the container, it MUST be restarted immediately in order for recognizing the new certificates   sudo docker restart opendj   Verify that the certificate is the correct one, execute on the machine that runs the Docker environments: openssl s_client -connect localhost:636 -showcerts   Load the .ldif Use e.g. JXplorer and connect   Select the opendj node LDIF > Import File (my demo breakingbad.ldif is attached to this post) Skip any warnings and messages and continue to import the file   ThingWorx Tomcat If ThingWorx runs in Docker as well, a pre-defined keystore could be copied during image creation. Otherwise connect to the container via commandline: sudo docker exec -it <ThingworxImageName> /bin/sh Tomcat configuration cd /usr/local/openjdk-8/jre/lib/security openssl s_client -connect 10.164.132.9:636 -showcerts Copy the certifcate between BEGIN CERTIFACTE and END CERTIFICATE of above output into opendj.pem, e.g. echo "<cert_goes_here>" > opendj.pem Import the certificate keytool -keystore cacerts -import -alias opendj -file opendj.pem -storepass changeit   ThingWorx Composer As the IP address is used (the hostname is not mapped in Docker container) the certificate must have a SAN containing the IP address     Only works with the TWLDAPExample Directory Service not the ADDS1, because ADDS1 uses hard coded Active Directory queries and structures and therefore does not work with OpenDJ. User ID (cn) must be pre-created in ThingWorx, so the user can login. There is no automatic user creation by the Directory Service. Make sure the Thing is Enabled under General Information   Appendix LDAP Structure for breakingbad.ldif cn=Manager / Password123456 All users with password Password123456    

In case it's useful for anyone, I successfully used the Thingworx Importer to import an Entity using version 8.4.1. The import command was in a CURL script (can also be run using e.g. Cygwin on Windows) and the entity data was contained in an XML file. The XML file is attached to this post, and the CURL script is copied into the bottom of the post body.   Notes on using the script: Copy CURL code into import.sh You might need to change the line endings to UNIX, e.g. in Notepad++ menu option Edit > EOL Conversion > Unix Give permissions to run import.sh (chmod +x import.sh) ./import.sh >>>>>>>>>>>> #!/bin/bash APPKEY="2e6704c0-XXXX-XXXX-XXXX-a1589e387d1a" TWX_HTTP_PORT="8018" FILE_PATH_TWX="Things_testImport.xml" PROTOCOL="http://" IP="localhost" URL="$PROTOCOL""$IP"":""$TWX_HTTP_PORT""/Thingworx/Importer?purpose=import" curl -X POST -H 'appKey: '$APPKEY \ -H 'Content-Type: multipart/form-data' \ -H 'Accept: application/json' \ -H 'x-thingworx-session: true' \ -H 'X-XSRF-TOKEN: TWX-XSRF-TOKEN-VALUE' \ -F 'upload=@'$FILE_PATH_TWX \ $URL <<<<<<<<<<<<   Also TWX Importer is explained in this support article.

When predicting a Boolean goal such as Failure in the next hour or any other goal that has a yes or no answer, Thingworx Analytics(TWXA) models will output a 'risk' of the event occurring. TWXA will intelligently pick a threshold beyond which that risk warrants attention. 1. In Analytics Builder, click on the export button 2. This will export a PMML model and download it for you 3. Open up the PMML model, in the output section, you will find a condition that explains the threshold that was selected by TWX Analytics.   In this example case, TWXA chose 0.5 as the best Threshold.   Note: The export button will only be available in Builder for TWXA 8.4+.

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.

Warning This post is quite long, has various chapters and you might get bored reading it. If you just want a summary read the "Use case" and "Conclusion" chapter - and maybe the "Required Logic" chapter, because I made a cool graph for it. The rest is all about implementation... Introduction I recently had the opportunity to deliver a ThingWorx training for Saint Gobain. One of the use cases for their ThingWorx application is monitoring machine errors and outages on the production line. If an outage or error status is triggered, the machine operator will see a popup on the monitoring screen where he is forced to select a root cause. This root cause will then be persisted in ThingWorx for more data transformation, analytics and reporting - like cost analysis or optimization opportunities. During the training we were also discussing on how such a forced root cause monitoring can be implemented via Mashups and the usage of modal popups. I've compiled the details into this post as it might also interest other developers. The ThingWorx Entities I'm using in this example can be downloaded from here Note: I'm using the word "Alert" here - but not in the context of a ThingWorx Property Alert... just beware to not be confused due to the wording. Use Case One of the requirements for Saint Gobain's IoT Solution was an interactive alert monitoring directly in the factory on the production machines. Let's say the machine has stopped, the root cause should be recorded. For this an interactive popup will be displayed on the machine's monitoring display and an employee has to choose the root cause from a pre-defined list. This could be planned outages, e.g. for maintenance or unplanned outages, e.g. material jam. The root cause will then be recorded and a history of outage causes can be stored in a ThingWorx value stream. This can then be later analyzed with e.g. ThingWorx Analytics capabilities to understand and optimize the machine's production capabilities and efficiency. As the root cause must be entered, the popup will be forced to be displayed when a certain condition / criteria is met - and it will only disappar when a root cause is chosen. The user should not be able to interact with any other elements of the Mashup and not be able to just close the popup. The popup will close itself and reset the initial condition once the root cause has been identified and chosen. Requirements Required Entities Required Logic Note: Just to make it easier to manage and export Entities, I will add all of the created elements in a new Project called RootCausePopups. All of the elements will have a "rcp_" added in front of their name - just to make it easier for me to find and identify them. Implementation Before we start - set a Project Context Create Entities Create a Popup Mashup Create the Main Mashup Testing the Mashups Conclusion Certain conditions (like the state of a checkbox) can be used to trigger modal popups. A modal popup forces a user interaction and the interaction will not offer any other option until a choice is made. With these parameters it's easy to have mandatory reaction from users when it's important to capture data which rely on the analysis of an engineer or a user - e.g. reasons for machine outages. Using this technique there's not much training required for staff, other than pushing a button with an option of their choice - this saves quite some time in capturing data in any other way (e.g. updating Excel files or manual pen-and-paper techniques). As this data is now part of the ThingWorx instance it can be used for further transformation, analysis or just for monitoring purposes There's of course more possibilities when it comes to states and formatting which would exhaust the context of this post - but feel free to explore... In the example we wouldn't need the textbox, but it's there to demonstrate if the correct values are persisted or not In the example we could of course also set the visibility of the checkbox to false, so that we would only see the popup and the Grid holding historic information We could also create different StateDefinitions to color-format / text-format the input differently from the output in the Grid If you found this interesting (and actually made it to the end of this post) - feel free to play with this concept a bit more... The dependencies might seem a bit difficult, but it should be easy to implement and to adjust to your own ideas and requirements.

Disclaimer: This post does of course not express any political views.   Pie Chart Coloring   In ThingWorx Pie Charts use a default color schema based on the DefaultChartStyle Definitions. These schemas are using fixed numbering and coloring systems, e.g. 1 is blue, 2 is green, 3 is red and so on. All Pie Charts will be rendered with these colors in the same order, no matter which data the chart is using. Visualization of data with the default colors might not necessarily help in creating an easy to read chart.   Just take a look at the following example with the default color schema. Let's just take political parties - as they are usually associated with a distinct color - to illustrate how the default color schema will fail depending on the data displayed.         In the first example, just by sheer coincidence the colors are perfectly matching the parties. When introducing a new party to the pool suddenly the blues are rendered green and the yellows rendered light-blue etc. This can be quite confusing, especially on election night 😉   Custom Color Schema   PoliticalParties Thing   To test a custom color schema, we first need to create a new Thing: PoliticalParties as a GenericThing Add a dataset property with the following PoliticalParties DataShape.         Save the Thing and set the InfoTable to:   Key Value The Purples 20 The Blues 20 The Greens 20 The Reds 20 The Yellows 20 Others 20   Number values don't actually matter too much, as the Pie Chart will automatically distribute them according to their percentage.   PoliticalParties Mashup   Create a new Mashup and add a PieChart to the canvas. Bind the PoliticalParties > GetPropertyValues > dataset to the Data input of the Widget. Ensure to set the LabelField to key and the ValueField to value for a correct mapping.     Save the Mashup and preview it.   It should show a non-matching color for each party listed in the InfoTable.   Custom Styles and States   Create new custom Style Definitions for each political party. As the Pie Chart is only using the Background Color other properties can stay on the default. I chose to go with a more muted version of the colors to make the chart easier to look at.         With the newly defined colors we can now generate a new State Definition as follows:       The States allow to evaluate the key-Strings in the Thing's InfoTable and assign a Style Definition depending on the actual value. In this definition we map a color schema based on the InfoTable's key-value to create a 1:1 mapping for the Strings.   This means, no matter where a certain party is positioned in the chart it will be tinted with its associated color.   Refining the Mashup   Back in the Mashup, select the PieChart. In the ColorFormat property choose the newly created State Definition.     Save the Mashup and preview it. With the States and Styles applies, colors are now displayed correctly.       Even when changing positions and numbers in the original InfoTable of the PoliticalParties Thing, the chart now considers the mapping of Strings and still displays the colors correctly.