Published Release Notes

Real-time event processing

Real-time event processing is a mechanism that can be used to analyze a high velocity stream of events entering the system and derive conclusions from them. It is possible to define metrics, measurements, and conditions under which subsequent trigger events are generated.

For details, see Real-time event processing.

Text analytics on the Pega 7 Platform

The Pega 7 Platform offers users the capability to analyze units of text, extract quality information, and translate it into business intelligence. The text analytics functionality aims to determine the positive, negative, and neutral polarities (tone) of subjective sentences. It also provides mechanisms for categorizing units of text and classifying text elements into predefined categories. You can use it to analyze text-based content including news feeds, emails, and postings on social media streams, such as Facebook, Twitter, and YouTube.

For more information, see Introduction to text analytics on the Pega 7 Platform.

Updates to the Data Flow rule

It is now possible to use report definitions as primary and secondary sources in the Data Flow rule. The data flows that are sourced by a report definition can be distributed on multiple nodes. When using a database table data set as the destination of the data flow, you need to select one of the save options for the data set (insert records or insert and update records).

Updates to the Event Strategy rule

When you design event strategies, you can use the Filter shape for basic arithmetic and text expressions. You can also specify start conditions for the tumbling and sliding windows.

Updates to the Predictive Model rule form

The Predictive Model rule form provides the XML schema preview for uploaded PMML models. You can use this preview to view the structure of a model and to correct errors before saving the rule. When the model contains errors, they are displayed in the Errors section at the bottom of the rule.

Updates to the Strategy rule

The Strategy tab of the Strategy rule form contains a new Test runs panel from which you can run simulations and test strategies for single and multiple customers. When you test strategies for multiple customers, you can identify the most popular propositions offered to the customers, check the overall strategy performance, and examine the performance of the individual components.

New types of data sets

There are three new types of data sets:

• Facebook - Required for connecting with the Facebook API
• Twitter - Required for connecting with the Twitter API
• YouTube - Required for connecting with the YouTube Data API

Use these data sets when you create instances of the Free Text Model rule and you want to analyze the text-based content on social media streams such as Facebook, Twitter, or YouTube.

Problem with truncating Decision Data Store data set

The Truncate operation for the Decision Data Store data set may cause timeout exceptions. This problem is caused by the Apache Cassandra database that waits until compaction tasks finish before it can truncate the data set.

Recommendation:

Repeat the Truncate operation until it is successful.

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.