Enhanced adaptive model reporting
Valid from Pega Version 7.4
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
Valid from Pega Version 7.4
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
Valid from Pega Version 7.4
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.
Decisioning services now use default node classification
Valid from Pega Version 7.4
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
Valid from Pega Version 7.4
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
Valid from Pega Version 7.4
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
Valid from Pega Version 7.4
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
Valid from Pega Version 7.4
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.
Configure the window size at run time in event strategies
Valid from Pega Version 7.4
You can now configure an Event Strategy rule to dynamically define the window size at run time by using a property value of the incoming record. To configure the window shape for dynamic size setting, you can use any property from the inheritance path that is available to the event strategy. This enhancement provides greater flexibility for strategy designers and broadens the scope of business scenarios in which event strategies can be applied.
For more information, see Event Strategy rule form - Completing the Event Strategy tab and Dynamic window size behavior.
Aggregate data in interaction history summaries
Valid from Pega Version 7.4
You can now group, aggregate, and filter interaction history data in a single strategy component. By using interaction history summaries, you can create refined data sets that simplify strategy frameworks and accelerate decision-making. Aggregated data sets are easier to process, manage, and troubleshoot.
For more information, see Data Sources landing page