Published Release Notes

Decisioning services now use default node classification

Decisioning services have been integrated with default node classification on Pega^® Platform to provide a unified way of creating and initializing services. As a result of the integration, the Data Flow service has been divided into Batch and Real Time services to better handle different types of data flow runs. You can now specify separate subsets of Data Flow nodes for batch data flow runs and real-time data flow runs to divide the workload between these two subsets.

For more information, see Node classification, Data Flows landing page, and Services landing page.

Train machine learning models for extracting named entities and detecting intents

Data scientists can train machine learning-based text extraction and intent detection models by using the Analytics Center. With text extraction, you can train a Conditional Random Fields (CRF) model to detect whether the content contains specified entity types such as person names, company and organization names, locations, dates and times, percentages, and monetary amounts. For intent detection, you can train a maximum entropy model to understand user intentions expressed in written content. With these two new capabilities, you can quickly react to customer queries and comments by taking appropriate action against the information that you extracted.

For more information, see Creating machine learning-based text extraction models and Creating machine learning-based intent analysis models.

Label changes for text analytic models

The classification analysis label has changed to topic detection and the entity extraction label has changed to text extraction. Also, the sentiment analysis, topic detection, and intent detection labels are now located under Text Categorization in the Analytics Center. These name changes reflect industry standards and provide a clearer distinction between different types of text analytics models in Pega^® Platform.

For more information, see Text analytics models.

Automatically detect the most important themes in text

Text analyzers can now automatically recognize the most important concepts that are expressed in text and mark such concepts as entities of type auto_tags. You can use auto_tags to group similar content by its themes and reduce the dimensionality of text to the most important features.

For more information, see Text extraction analysis.

New privilege required to access the Search landing page

After upgrading to Pega^® Platform 7.4, users who do not have the pxAccessSearchLP privilege cannot access the Search landing page. The pxAccessSearchLP privilege is automatically assigned to the SysAdm4 role. If you have other roles that require access to the Search landing page, you must add the pxAccessSearchLP privilege to those roles.

For more information about assigning privileges to roles, see User privilege authorization. (Link to: basics/v6portal/landingpages/accessmanager/customizeprivilegestab.htm)

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