Published Release Notes

Compact style sheet for creating PDF files

You can now use a compact style sheet when you create PDF files by using the Create PDF smart shape in a case type or when you use the HTMLtoPDF activity. This style sheet overrides the style sheet that is used by the harness skin and correctly renders certain elements such as headings and inline grid tables. It also renders PDF files more quickly.

For more information, see Creating PDF files by using a compact style sheet and PDF file rendering scenarios.

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.

Touch response supported on mobile devices

When displayed on a mobile device, all active elements of the Pega^® Platform user interface now respond to touch by changing their appearance. No additional configuration is available or necessary.

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.

Setting a build version of the custom mobile app

When you build a custom mobile app, you can now configure a build version and leave the custom mobile app version unchanged. This way you can upload an updated custom mobile app to the Apple App Store and Google Play Store without having to increment the app version.

For more information, see Setting the app build version.

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.

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