Published Release Notes

Data pages can source information from a robotic process automation

When you use robotic process automation (RPA), you can now configure data pages to source information from robotic automations so that you can connect your Pega® Platform application to legacy applications in your enterprise. By using automations to retrieve data and save it to a data page, you can use data virtualization to encapsulate your Pega Platform data model from the physical interface of a legacy system against which the automation is running.

For more information, see Obtaining information from robotic automations.

Manage propositions in multitenant environments

Proposition Management is now supported in multitenant environments of Pega® Platform. The DisablePropCache Dynamic System Setting switches off the use of a proposition cache. When you import propositions in strategies, you can import only versioned propositions (Decision Data rules). Unversioned propositions are not supported under this setting, but these can be converted to versioned propositions.

For more information, see Unversioned propositions, Synchronization of the proposition cache, and Converting groups with unversioned propositions.

Configure the window size at run time in event strategies

You can now configure an Event Strategy rule to dynamically define the window size at run time by using a property value of the incoming record. To configure the window shape for dynamic size setting, you can use any property from the inheritance path that is available to the event strategy. This enhancement provides greater flexibility for strategy designers and broadens the scope of business scenarios in which event strategies can be applied.

For more information, see Event Strategy rule form - Completing the Event Strategy tab and Dynamic window size behavior.

Aggregate data in interaction history summaries

You can now group, aggregate, and filter interaction history data in a single strategy component. By using interaction history summaries, you can create refined data sets that simplify strategy frameworks and accelerate decision-making. Aggregated data sets are easier to process, manage, and troubleshoot.

For more information, see Data Sources landing page

New shape on the strategy canvas

The Strategy rule has been enhanced with the Embedded strategy shape, which simplifies the configuration of decisioning strategies that simultaneously apply to multiple types of audiences, for example, Devices, Households, and Subscribers. The Embedded strategy shape eliminates the need to switch between various classes to create a substrategy for each audience type that you want to target. Now, you can perform all configuration tasks on a single strategy canvas.

For more information, see Strategy components – Embedded strategy and Multilevel decisioning strategies.

New interaction history attribute

Pega^® Platform 7.4 introduces the pySubjectType attribute that is used in interaction history aggregations. This attribute is populated for interaction history records that were created in release 7.4. For records that originated in earlier releases, the attribute must be set in the following scenarios:

• Single-level decisioning frameworks that use interaction history.
• Multi-level decisioning frameworks where interaction history is used by two or more levels of strategies that are defined on different classes.

For the single-level scenario, configure the Dynamic System Setting that sets the pySubjectType attribute when your framework reads interaction history records. The value of this Dynamic System Setting becomes the name of the customer class.

For the multi-level scenario, update the database table for all strategy levels manually. For each level, make sure that the value in the Subject Type column is set to the name of the class for the corresponding strategy. For example, the value for the top level strategy should be set to the name of the class of that strategy.

For more information about interaction history aggregations, see Data Sources landing page

For more information about multi-line strategies and contexts, see Strategy components - Embedded strategy

HBase data set type becomes resumable

HBase data set has been enhanced to be resumable. As a result, data flow runs that reference the HBase data set as their primary data source are now resumable. You can pause and resume such data flow runs.

For more information, see the Resumability of data flow runs section in Data flow run updates.

Data pages can source information from a robotic desktop automation

You can now configure data pages to source information from robotic desktop automations (RDA). Using an RDA to source a data page allows you to connect your Pega Platform™ application to any application that is accessible from an end-user's desktop. By using automations to retrieve data and load it into a data page, you can use data virtualization to separate your Pega Platform data model from the physical interface of a legacy system against which the automation is running.

For more information, see Obtaining information from robotic automations.

Use repositories as sources for File data sets

You can configure remote repositories, such as Amazon S3 or JFrog Artifactory, or a local repository, as data sources for File data sets. By referencing an external repository from a File data set, you enable a parallel load from multiple CSV or JSON files, which removes the need for a relational database for transferring data to Pega Platform™ in the cloud.

For more information, see Creating a File data set record for files on repositories and Configuring a remote repository as a source for a File data set.

Define a taxonomy by using the Prediction Studio interface

Create a topic hierarchy and define keywords for each topic in Prediction Studio faster and more intuitively than by editing a CSV file. If you have already defined a taxonomy in a CSV file, you can import that file and modify existing topics and keywords by using the Prediction Studio interface.

For more information, see Creating-keyword-based topics for discovering keywords and Tutorial: Configuring a topic detection model for discovering keywords.