Published Release Notes

Support for controlling binary file upload in Pega Mobile Client

The Client Store API of Pega^® Mobile Client has been modified so that you can suspend and resume the process of uploading binary files to the server. You can also check the state of the file uploading process. As a result, custom mobile app users can process their work items without having to wait for the synchronization process to finish.

For more information, see Pega Mobile Client API methods for controlling binary data upload and Client Store API.

Support for large binary data item storage in Pega Mobile Client and offline case attachments in Pega Platform

Pega^® Mobile Client can now store and synchronize large binary data items and their corresponding actions from the action queue on Android and iOS devices. As a result, custom mobile app users can view, attach, and delete data files, whether the device is online or offline. For this function to work, you must enable offline case attachment support, which allows your custom mobile apps to synchronize case attachments between the Pega Platform and the mobile device.

For more information, see Enabling offline case attachments support and Client Store API.

Enhanced adaptive model reporting

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

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.

Improvements in the process engine for offline-enabled applications

The offline process engine includes several improvements. To provide more options when you develop an offline-enabled application, a decision shape as the first shape in a flow and parameters for the CreateNewWork action are now supported. Offline-enabled applications now also display information about the last data synchronization.

For more information, see Flow processing in offline mode.

Store and scale the processing of Stream data records on multiple nodes

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

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.

Touch response supported on mobile devices

When displayed on a mobile device, all active elements of the Pega^® Platform user interface now respond to touch by changing their appearance. No additional configuration is available or necessary.

Train machine learning models for extracting named entities and detecting intents

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.

Setting a build version of the custom mobile app

When you build a custom mobile app, you can now configure a build version and leave the custom mobile app version unchanged. This way you can upload an updated custom mobile app to the Apple App Store and Google Play Store without having to increment the app version.

For more information, see Setting the app build version.