Published Release Notes

Data synchronization does not resume after mobile app restart

If an offline-enabled mobile app that you build with Pega Infinity Mobile Client™ is stopped during an initial data synchronization session, the data synchronization does not resume when the mobile app is restarted. Users must not stop the mobile app before initial data synchronization finishes.

For more information about data synchronization, see Offline capability and Guidelines for creating an offline-enabled application.

MBeans deprecated in favor of Pega API

The use of MBeans for cluster management has been deprecated, although MBeans will continue to function for legacy deployments. The recommended best practice for automating system management is to use the Pega API. For more information, see Pega API.

Enhanced data flow performance monitoring

The performance alert log now provides more detailed information about the data flows in your application. You can view performance details for data flows that are currently running and data flow runs that have completed successfully. You can also view a performance summary for data flow runs that are running for too long or have failed.

For more information, see:

• PEGA0082 alert: Dataflow started
• PEGA0083 alert: Dataflow complete
• PEGA0062 alert: Data flow execution time above threshold
• PEGA0072 alert: Data flow run failed

Text analytics models editing and versioning

Pega Platform™ now supports editing and updating training data for text analytics models.

Pega Platform also supports the versioning of text analytics models. When you update the model, Prediction Studio creates an updated model version. You can then switch between the model versions.

Upgrade impact

In versions of Pega Platform earlier than 8.3, the training data for text models was stored in the database. In Pega Platform version 8.3 and later, the training data for text models is stored in Pega Repository. You cannot build new models without setting the repository. After the repository is set, all text models are automatically upgraded and will work normally.

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

After a successful upgrade, set the repository in Prediction Studio before building or updating any Natural Language Processing (NLP) models.  In Prediction Studio, click Settings > Text Model Data Repository.

For more information, see:

• Increase the accuracy of text analytics models by adding feedback data (8.3)
• Updating training data for text analytics models

Ability to support advanced device features with Pega Infinity Mobile Client

Pega Infinity Mobile Client™ now provides a development kit that you can use to make mobile apps support advanced device features that are typically available to native applications, for example, an embedded laser scanner or a projector module, and integrate functions of external software solutions, for example, a cloud-based file storage.

For more information about enhancing mobile apps with device feature support, see the documentation for the development module in the latest Pega Infinity Mobile Client distribution package on Digital Delivery.

Text analytics models migration

Pega Platform™ now supports the exporting and importing of text analytics models. For example, you can export a model to a production system so that it can gather feedback data. You can then update the model with the collected feedback data to increase the model's accuracy.

Upgrade impact

In versions of Pega Platform earlier than 8.3, the training data for text models was stored in the database. In Pega Platform version 8.3 and later, the training data for text models is stored in Pega Repository. You cannot build new models without setting the repository. After the repository is set, all text models are automatically upgraded and will work normally.

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

After a successful upgrade, set the repository in Prediction Studio before building or updating any Natural Language Processing (NLP) models.  In Prediction Studio, click Settings > Text Model Data Repository.

For more information, see:

• Increase the accuracy of text analytics models by adding feedback data (8.3)
• Exporting text analytics models
• Importing text analytics models

Support for regular performance alerts

You can now specify how often Pega Platform™ sends you performance alerts about SLA violations in data flow runs. By default, the interval is 5 seconds for single case data flows and 5 minutes for batch and real-time data flows.

The following dynamic system settings control the alert interval:

• Single case runs: dataflow/singlecase/alert/throttleTime
• Batch runs: dataflow/batch/alert/throttleTime
• Real-time runs: dataflow/realtime/alert/throttleTime

For more information, see PEGA0062 alert: Data flow execution time above threshold.

Automatic model training when mapping entities to case properties in Email Bot

You can now initiate automatic feedback to entity models in Pega Email Bot™, during manual mapping of email content to a case property.

To enable automatic feedback, you set the Work-Channel-Triage.pyIsRuntimeFeedback rule to true in Pega Platform™. By default this feature is disabled. Enabling this feature ensures that the email bot is more responsive by automatically copying detected entities, such as names, locations, dates, and ZIP codes, to case type properties of a case type.

For more information, see Email triage, Email channel NLP model and Enabling the NLP model training for the email channel.

Set the status of a case on stage entry

You can now set the status of a case when it enters a stage. By setting the status of a case on stage entry, you can communicate to stakeholders how close a stage is to resolution. For example, if a case is in the approval stage, you can set the status of the case to "Under review" when the case enters the stage.

For more information, see:

• App Studio – Changing the status of a case
• Dev Studio – Changing the status of a case

Resolve child cases with the parent case

You can now configure a stage to resolve open child cases when the parent case is resolved. Resolving child cases on the resolution of the parent case ensures that no child cases are left open when the parent case reaches the end of its life cycle.

For more information, see:

• App Studio – Configuring case resolution
• Dev Studio – Configuring case resolution