This is defined by the Choose-Best-Ruleset algorithim which is used by both App Studio and Dev Studio. 

This algorithm uses the application stack, open rule sets, rule set of the classes of the rules and branch preferences. Using Branch Development preferences, the App Studio developer can control the application layer a rule will be placed in.

1. If rule exists it will attempt to update in place
2. A new rule will be created in the ruleset where the Class containing the rule is defined
3. If previous rulesets are not valid, will attempt the highest ruleset in a layer.

This allows a user working in implementation App to make changes to a build on framework app. 

We have technical materials that outline the process if details on Choose-Best-Ruleset are needed.

LINK TBA

The extension guide for Developer Assistant panel is included in the 8.8 release.

LINK TBA

The Application Security Checklist is a final rule, as they represent the minimum security checks that should be checked for every app.

You can author your own application guides. 

It does allow for efficient reporting, however the main objective is to avoid locking and the need to keep data synchronised across multiple object instances.

• The index table Key is a GUID, but
• The To ID and From ID are optimized for reporting.

Yes, several data pages are created depending on the configuration. Data pages are created to get an instance and to get a list of instances given a To ID or a From ID.

Use cases that data pages address:

1. Get list of items related to me
2. Open a specific relationship

Is there be any difference between REST and ODATA configurations?

If I already have some service configured with REST can I change to ODATA?

OData is a set of standards in implementing REST protocols so the changes required will depend on the vendor who provides the endpoints.

It is possible that you would not need any changes to an existing implementation but if you do, the wizard will cut the time required to do so from hours to minutes.

CRUD operations in the Constellation applications are using the Constellation API (DX API).

• Classes inheriting from Work- will use the /cases endpoints;
• Classes inheriting from Data- will use the /data_views endpoints. 

We are generating a RBAC rule on the backend. You can open this rule in Dev Studio if you need to add more granular access controls like conditional class access. The experience in App Studio allows you to control the "large bucket" access like read/write/delete.

This is a great example of how both App Studio and Dev Studio can be used in tandem when the complexity of your use case requires.

Most of the "setup" for Process AI in a case can be performed directly in App Studio and Prediction Studio. Of course there will always be tasks that require LSA/SSA guidance, such as configuring integrations with data sources.

The Low-Code Factory for Citizen Developement provides some guidance on setting up a citizen development program.

There can be multiple predictions in the same case, each predicting different outcomes. These are not conflicts.

Depending on the use case, one can also call on a single prediction several times during the lifetime of a case, the likelihood of an outcome can change over the lifetime of the case as more case data is collected and as the prediction model learns more over time.

For Process AI, the monitoring of predictive/adaptive models includes a set of notifications. See also Viewing Prediction Studio notifications.

They are different. Both these solutions work towards optimizing processes and reinforce each other. Pega’s process mining capabilities come from the acquisition earlier this year of Everflow, which is a powerful and broad solution for process mining. We have an early adopter program for Pega clients who run a Center of Excellence. We are planning to provide process mining in an upcoming release.

See https://www.pega.com/products/platform/process-mining for a quick overview of the functionality.

Adaptive models used to learn in real time, updating when input data updated. Why are we adding a pooling interval for adaptive models?

In a production environment, the adaptive models will adapt their behavior as new evidence is being recorded.

In other environments, QA can be set up with expected results.