Workflow Data Scope Integration for Structural Change Management
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Solution Overview
Problem
Existing business process management systems face challenges in dynamically managing workflow changes and data visibility, particularly in ensuring data visibility correctness and minimizing redundancy during structural changes.
Innovation Solution
The method involves identifying affected data scopes in a baseline workflow due to structural changes, comparing them with fragment workflows, and integrating these changes to create an integrated workflow while applying data scope operations such as add, remove, expand, contract, split, or merge operations to maintain data visibility correctness and reduce redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If workflow structural changes are implemented to enable flexible business process management, then adaptability is improved, but data visibility correctness and redundancy control become more difficult to maintain
Solution Approach 1:
The system performs preliminary actions by automatically identifying affected data scopes before integrating workflow changes. It compares baseline and fragment workflows to determine which data scopes need modification, then applies appropriate data scope operations (add, remove, expand, contract, split, merge) in advance to maintain data visibility correctness throughout the integration process.
2Reliability
If data scope operations are applied to maintain data visibility correctness during workflow changes, then data integrity is preserved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting and resolving data scope conflicts during workflow integration. It independently identifies affected data scopes, determines appropriate operations, and applies them without requiring manual intervention, thereby maintaining data integrity while managing system complexity through automated processes.
3Reliability
If manual data scope management is performed during workflow integration, then data visibility can be controlled, but processing time and labor requirements increase
Solution Approach 1:
The system eliminates manual data scope management by performing self-service automation. It automatically compares baseline and fragment workflows, identifies affected data scopes, determines the appropriate data scope operations, and executes them autonomously, significantly reducing processing time and labor requirements while maintaining reliable data visibility control.
Data Source
AI summary
A method includes providing a baseline workflow as an electronic representation of an actual workflow, the baseline workflow including baseline tasks, data items, and baseline data scopes, and providing a fragment workflow as an electronic representation of an actual fragment workflow, the fragment workflow including at least one fragment task, and at least one fragment data scope. A baseline data scope is identified as an affected data scope based on a structural change operation, the baseline workflow and the fragment workflow, and the affected data scope is compared to the at least one fragment data scope to identify at least one change operation. The fragment and baseline workflows are integrated based on the structural change operation to provide an integrated workflow, and the at least one data scope change operation is executed to provide at least one integrated data scope in the integrated workflow.