Published Release Notes

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.

Supported artifacts section removed from repository rule form

The Supported artifacts check box has been removed from the repository rule form. Developers can use repositories to provide centralized storage, versioning, and metadata support for application artifacts.

For information about repository connections, see Creating a repository for file storage and knowledge management.

Support for character data type on local variables in activities deprecated

Support for the character data type for local variables in activities is deprecated. Use the string data type instead.

For more information about activities, see Activities.

When rule might not resolve against @baseclass

In the rare case in which a step page context is null, or the pxObjClass associated with the step page is empty, the system no longer attempts to resolve the when rule against @baseclass. As a result, you might see more rule not found errors. Check the step page context and class and ensure that they are not null or empty. In all other cases, if a when rule is not found in any other part of the class hierarchy, the system attempts to resolve it against @baseclass.

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.

Improved performance of decision strategies

Strategy rule performance has been improved through the implementation of a new engine. You can perform single and batch test runs to analyze strategy performance, locate and prevent potential issues, and optimize strategy components. Test runs now support data sets and data flows with multiple key properties. The redesigned Test run panel improves the display of information and highlights the most immediately relevant details.

For more information, see Configuring a single case runs and Configuring a batch case runs.

Extract summaries from the analyzed text

You can now configure a Text Analyzer rule to extract information-rich blocks of text from the analyzed content and combine them into a comprehensive and coherent summary. By summarizing large documents, such as emails, you can facilitate making business decisions without having to read an entire document. In Text Analyzer rules, you can combine summarization with other types of text analysis, such as topic or entity detection, to extract the full context from a message.

For more information, see Configuring text extraction analysis and Tutorial: Extracting email context with Text Analyzer rules.

Additional adaptive model predictors based on Interaction History

Customer interactions are now automatically used in adaptive models to predict future customer decisions. For example, a phone purchase registered in Interaction History allows an adaptive model to predict that a customer is more likely to accept supplementary coverage for a new device. Such interactions, collected in a predefined Interaction History summary, are applied as an additional set of predictors in an adaptive model.

The aggregated Interaction History summary predictors are enabled by default for every adaptive model configuration.

For more information, see Enabling Interaction History predictors for existing adaptive models.

Update text analytics models instantly through an API

Use the pxUpdateModels API to automatically retrain text analytics models for which you gathered feedback as a result of the pxCaptureTAFeedback activity. The pxUpdateModels API provides an option to update the model with the latest feedback without having to open Prediction Studio. Instead, you can use the activity from your application, for example, through a button control.

For more information, see Feedback loop for text analytics.