Published Release Notes

Additional upgrade scripts for new columns

In Version 6.2, additional columns (Major, Minor, and Patch) containing ruleset Version information were added to 15 tables in the PRPC database. When upgrading from a pre-Pega 7 release, scripts must be manually run after the upgrade to populate these columns.

These scripts are located in the Resource Kit, under Additional Upgrade Scripts.

• If upgrading to a single schema, run both scripts.
• If upgrading to a split schema, run the _data script against the data schema and run the _rules script against the rules schema.

Choose the scripts for your database type:

• db2zos_rulesetversion_columns_data.sql
• db2zos_rulesetversion_columns_rules.sql

• mssql_rulesetversion_columns_data.sql
• mssql_rulesetversion_columns_rules.sql

• oracle_rulesetversion_columns_data.sql
• oracle_rulesetversion_columns_rules.sql

• postgres_rulesetversion_columns_data.sql
• postgres_rulesetversion_columns_rules.sql

• udb_rulesetversion_columns_data.sql
• udb_rulesetversion_columns_rules.sql

Database

Improvements to data handling were made for search indexing, agent processing, and filtering.  Scripts were provided for proper population of the database tables.

• Importing data from shared obj-external tables is now supported.
• A validation warning will now display when an unexposed property is used in filter or join conditions.
• A Global Search index may now be created across two databases.
• Agent processing on multiple nodes has been improved.
• Improvements were made to controls in Report Definition pages which are used in a dynamic grid.
• Filtering functionality involving special characters has been enhanced.
• Scripts have been provided to populate the RuleSetVersion Major, Minor, and Patch columns.

Decision Management

This release contains a number of fixes that improve the configuration and execution of Batch decisions – specifically, a number of usability improvements and addresses some issues when creating strategies on IE8.  

• Batch Decision functionality has been enhanced in the NBAM environment.
• In NBAM, users can run the strategy execution batch based on the Seed list class instead of the customer class. 
• Interaction data will now work even if Action or Organization  dimensions do not have values set.
• Running a strategy which includes a Data Import of a structured page reference will work in batch mode.
• Security was enhanced on the VBD Planner.
• The Access of Role to Object rules have been enhanced to allow users to create new Dimension Operator records.
• The NBAM Segment On Canvas will now fetch the SegMap data, even when the generated extended segment class has not been added to the Pages and Classes of the strategy.  The SegMap property will (if it exists) be added to a list of properties to fetch, even if the strategy indicates that it is not used.
• When a strategy is defined on a customer class, users may now specify the list of required properties to fetch for the input definition.
• When creating a new strategy, there is now a section to add a Strategy Results class like Business Issue or Group.

System Management

Refinements were made to out-of-place upgrade documentation and processes, as well as for how handling is performed for all of the database vendor specific nuances related to a deploying into a split schema environment.

• A script was created to run the data schema upgrade for z/OS systems.
• In a split-schema configuration, indexing has been enhanced.
• The product installer has been enhanced.
• The stand-alone Static Assembly Utility does not require a password for its process.
• The UpdateManager tool has been improved.

Specify the scope for rolling back rules and data to a restore point

Create restore points to save the state of all rules and data in your system at a significant point in time, for example, before you import an application. Roll back to that restore point to return the system to that state. Now, you can filter which rule and data instances are returned to their previous state:

• System: Roll back every rule and data instance that has a history record.
• User: Roll back rule and data instances modified by a specific user. If any rule was changed by more than one user, you will see an error message and must use the system rollback.
• Application: Roll back rule and data instances in a specific application.

For more information, see Using restore points to enable error recovery.

Connect to Amazon SageMaker models in Prediction Studio

Make the most of your custom Amazon SageMaker models in Pega Platform™ by connecting to the models in Prediction Studio. You can then run the Amazon SageMaker models as part of your decision strategies.

For more information, see Enrich your decisioning strategies with H2O and Amazon SageMaker predictive models (8.4).

Import H2O models to Prediction Studio

Make the most of your custom H2O models in Pega Platform™ by importing them to Prediction Studio. You can then include the H2O models in your decision strategies.

For more information, see Enrich your decision strategies with H2O and Amazon SageMaker predictive models (8.4).

Support for auditing adaptive model decisions

Pega Platform™ now stores all adaptive model scoring data so that you can identify the source of each decision, such as the exact model version that was used for scoring. With this feature, you can ensure that your application is auditable, transparent, and in compliance with regulatory requirements related to using adaptive models.

For more information, see Configuring the Adaptive Decision Manager service.

Create predictions in Prediction Studio

Predict customer behavior and business events by creating predictions. To create a prediction, you answer a series of questions about what you want to predict. For example, you can create a prediction to determine the likelihood of customer churn.

For more information, see Create predictions in just a few clicks (8.4).

Predictive models monitoring

In Prediction Studio, you can now monitor the predictive performance of your models to validate that they make accurate predictions. Based on that information, you can re-create or adjust the models to provide better business results, such as higher accept rates or decreased customer churn.

For more information, see Monitoring predictive models.