Published Release Notes

Ability to include all properties in search results

You can now include all properties in a class in search results so that they can be used in report filtering. When the Enable search results for all properties option is selected on the Custom Search Properties rule form, all top level and embedded page scalar properties for the class are indexed and returned in search results, and all embedded page list, page group, value list, and value group properties for the class are indexed but not returned in search results. Selecting this option might affect performance.

In addition, by default, properties defined in the Data-Tag-RelevantRecord instance for a class are indexed and returned in search results and do not need to be explicitly configured.

You must reindex search after selecting or clearing Enable search results for all properties and after changing the properties that are defined in the Data-Tag-RelevantRecord instance.

For more information, see Specifying custom search properties.

Enhanced adaptive model reporting

The new Model report replaces the Behavior and the Performance overview reports to improve report usability and provide consistent information. You can export your Model reports into PDF or Excel files to view or share them outside the Pega® Platform. The Model report also includes information on the groups of correlated predictors where the best performing predictor from each group is active in the model and other remain inactive; this information helps you understand why predictors are active or inactive.

For more information, see Generating a model report.

Use Kinesis data sets in Pega Decision Management

You can create Kinesis data set instances to connect to Amazon Kinesis Data Streams and use this data set in decision management for processing real-time streaming data. Integrating Kinesis data streams into Pega® Platform in the cloud provides a fault-tolerant and scalable solution for processing IT infrastructure log data, application logs, social media, market data feeds, and web clickstream data.

For more information, see Creating a Kinesis data set.

Store and scale the processing of Stream data records on multiple nodes

You can configure the Stream service on Pega^® Platform to ingest, route, and deliver high volumes of low-latency data such as web clicks, transactions, sensor data, and customer interaction history. You can store streams of records in a fault-tolerant way and process stream records as they occur. Add or remove Stream nodes to increase or decrease the use of the Stream service and optimize data processing.

For more information, see Stream service overview.

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.

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.

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.