Published Release Notes

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.

Apache Ignite is no longer supported

Apache Ignite is no longer supported and has been replaced by Hazelcast Enterprise Edition 3.10 in embedded mode. If you used encryption with Apache Ignite, you can configure encryption on Hazelcast Enterprise Edition in the same way that you configured it for Apache Ignite.

For more information about encrypting nodes, see Enabling encrypted communication between nodes.

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.

Automatically populate columns on import

If an imported archive file adds columns to existing tables, Pega Platform ™ can automatically expose and populate the new columns during the import. For more information, see Import wizard.

Automatically revalidate and save classes after upgrade

To automatically revalidate and save classes after an upgrade, edit the product rule definition. After you upgrade the application, the classes you specify are revalidated and saved under the application version context you provide. If the application version exists on the target system before the upgrade, the revalidate and save process is not called.

For more information about editing the product rule, see Specifying product rule deployment options.

Add and populate new indexes on import

If you import an archive that includes new indexes, use the Import wizard advanced mode to schedule a post-import job to automatically add and populate the new indexes. For more information, see Import wizard.

Resolve Job Scheduler and Queue Processor rules with the AsyncProcessor requestor type

Use the new AsyncProcessor requestor type to define a default dynamic context to resolve background rules such as Job Scheduler and Queue Processor rules. Switch to an application-based context after you create your own access groups, to resolve only the rules that are included in your application.

For more information, see AsyncProcessor requestor type.

Upgrading Adaptive Decision Manager data mart tables might fail

Issue: Upgrade from 7.3 to 7.3.1 fails if the data contained in the pxInsName column of the PR_DATA_DM_ADMMART_PRED_FACT table is longer than 128 characters.

Reason: During the Pega Platform™ upgrade from 7.3 to 7.3.1, data in the Adaptive Decision Manager (ADM) data mart tables is migrated from the PR_DATA_DM_ADMMART_PRED_FACT table to the PR_DATA_DM_ADMMART_MDL_FACT table. In Pega 7.3.1, ADM uses only the PR_DATA_DM_ADMMART_MDL_FACT table where the pxInsName property can store values that are 128 characters long. In Pega Platform 7.3, the pxInsName property in the PR_DATA_DM_ADMMART_PRED_FACT table can store values that are 255 characters long. If the pxInsName property contains values that are longer that 128 characters, the upgrade fails.

Resolution: Issue an ALTER TABLE statement to change the pxInsName column size to 255 characters and resume the upgrade. For example:

ALTER TABLE rules.pr_data_dm_admmart_pred ALTER COLUMN pxInsName TYPE varchar(255);

For more information, see Adaptive Decision Manager data model.