Published Release Notes

New Pulse gadgets for developers

Pulse for the Pega 7 Platform provides two new gadgets: pxContextFeed and pxActivityFeed.

The pxContextFeed gadget is a section that you can integrate with your application. Users can use this gadget to communicate with other users by creating and sharing posts within a given context. You can also customize the gadget or build your own Pulse interface by using a number of APIs.

The pxActivityFeed gadget aggregates all the activity feeds for the cases that a user follows without requiring that individual cases for posts be viewed.

For more information, see the help topic Pulse gadgets and custom APIs.

Support for large reference data in offline mobile apps

A data page in a Pega 7 Platform mobile application with offline capability enabled can now be marked as a large data page. This feature improves performance significantly when you have a large amount of data in which only individual records change.

In this situation, after the initial sync to the mobile app, only the changes in the large data page are synced instead of the entire large data page. The database table is created for such large data pages in the encrypted SQLite database on the device. Changes such as add, delete, or update are reconciled as row-level updates. Furthermore, you can use JavaScript to query against this data. A large referenced data page is supported by list-based components such as repeating dynamic layout, auto-complete, a dropdown list, and radio button.

Support for error handling API in offline mobile apps

During the processing of cases in offline-enabled mobile apps, you can use several JavaScript API methods to perform error handling. You can now determine whether an error condition results from the post-processing of a flow or a subflow. You can also set, clear, or obtain the number of error messages on a property or a page while working in offline mode, similar to when you are running a Pega 7 Platform desktop application. When you run a custom mobile app in offline mode, you can submit a form, but you cannot move the flow forward until all the errors on a page and in the page's properties are resolved.

For more information, see Error handling in offline mode and Error handling API in offline mode.

Build predictive models with the genetic algorithm

A genetic algorithm solves optimization problems by creating a generation of possible solutions to the problem. In Pega^® Platform you can utilize the functionality of genetic algorithms by using the genetic algorithm model in the Analytics Center portal. With this type of predictive model, you can create highly predictive, non-linear models by selecting the best performing model from the last generation.

For more information, see Creating models and Creating a genetic algorithm model.

System Management

Refinements were made to out-of-place upgrade documentation and processes, as well as for how handling is performed for all of the database vendor specific nuances related to a deploying into a split schema environment.

• A script was created to run the data schema upgrade for z/OS systems.
• In a split-schema configuration, indexing has been enhanced.
• The product installer has been enhanced.
• The stand-alone Static Assembly Utility does not require a password for its process.
• The UpdateManager tool has been improved.

Responsive UI works with the Mobile Mashup SDK

Responsive UI elements now work inside of a mobile application that has been integrated with the Mobile Mashup SDK for either an iOS or Android mobile device. Using a responsive UI with your mobile application reduces development costs, since one UI functions in a similar manner when accessed from both desktop and mobile devices, making it easier to deploy an application across multiple device types.

See Mobile Mashup SDK integration for iOS and Mobile Mashup SDK integration for Android for more information on using the Mobile Mashup SDK.

Text predictions simplify the configuration of text analytics for conversational channels

Enable text analytics for your conversational channels, such as email and chatbot, by configuring text predictions that manage the text models for your channels. This new type of prediction in Prediction Studio consolidates the AI for analyzing the messages in your conversational channels in one place and replaces the text analyzer rule in Dev Studio.

Through text predictions, you can efficiently configure the outcomes that you want to predict by analyzing the text in your channels:

• Topics (ticket booking, subscription cancellation, support request)
• Sentiments (positive, neutral, negative)
• Entities (people, organizations, airport codes)
• Languages

You can train and build the models that predict these outcomes through an intuitive process, and then monitor the outcomes through user-friendly charts.

For more information, see Predict customer needs and behaviors by using text predictions in your conversational channels.

Upgrade impact

Channels that you configured with text analyzers in the previous version of your system continue to work in the same manner after the upgrade to the current version. When you edit and save the configuration of an existing channel, the text analyzer rule is automatically upgraded to a text prediction. The associated text prediction is now an object where you can manage and monitor the text analytics for your channel. When you create a new channel in the upgraded system, the system automatically creates a text prediction for that channel.

What steps are required to update the application to be compatible with this change?

1. Enable the asynchronous model building and reporting in text predictions through job schedulers that use the System Runtime Context (SRC) by adding your application to the SRC.
   For more information, see Automating the runtime context management of background processes.
2. Enable model building in text predictions by configuring background processing nodes.
   For more information, see Assigning decision management node types to Pega Platform nodes.

Job Scheduler and Queue Processor rules replace agents

The Job Scheduler rule and the Queue Processor rule replace agents and improve background processing. The Job Scheduler rule is used for recurring or scheduled tasks, such as sending summary emails on weekdays. This rule can be run on all nodes or on specific node types according to your customized pattern. The Queue Processor rule is used for tasks with no recurrence-based pattern. It provides scalability and capability for immediate or delayed message processing, such as submitting status changes to an external system. You can use a standard queue processor or a dedicated queue processor, depending on your processing needs.

Pega Platform™ provides a set of default queue processors and job schedulers. Corresponding agents are no longer available.

For more information about Job Scheduler and Queue Processor rules, see Job Scheduler, Replacing an agent with a Job Scheduler rule, Queue Processor, Replacing an agent with a Queue Processor rule.

Discontinued support for list view and summary view rules

The list view rule and the summary view rule are deprecated, and support for these rules has been discontinued. Existing list view and summary view reports will continue to work on supported browsers. However, the last version of Internet Explorer that list view and summary view rules support is Internet Explorer 11.

You cannot create new list view or summary view reports. Instead, create list reports and summarized reports by using the report definition rule. To prevent display issues, re-create the custom list view and summary view reports that you need as report definition reports.

Internet Explorer 11 support

7.1.5 now provides support for Internet Explorer 11 (IE11). For those customers interested in information about IE11 support in previous releases, refer to the Platform Support Guide.