Smart Recovery System for Dynamic Workflow Task Re-execution
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Solution Overview
Problem
Current business process management systems lack the ability to dynamically update application data during workflow execution, often requiring cancellation and re-execution of entire workflows, which is inefficient and resource-intensive.
Innovation Solution
A smart recovery system for dynamic workflows that re-executes affected tasks with re-evaluation labels, allowing for selective re-execution of tasks based on data dependencies and predefined rules, without canceling the workflow instance, using a workflow engine and modeling editor that supports runtime re-evaluation and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the workflow instance is canceled and restarted with updated data, then data consistency is ensured, but workflow execution time and resource consumption increase
Solution Approach 1:
The workflow is segmented into individual tasks with identified data dependencies. When data changes occur, only the affected tasks are identified and re-executed based on their dependency relationships with the changed data, rather than re-executing the entire workflow. This segmentation allows selective re-execution of specific tasks while maintaining data consistency.
Solution Approach 2:
Instead of re-executing the complete workflow (excessive action), the system performs partial re-execution by identifying and re-executing only the subset of tasks that are affected by data changes. This partial action approach maintains data consistency for changed data while avoiding unnecessary re-execution of unrelated tasks, thereby reducing overall execution time.
2Reliability
If the entire workflow is re-executed to ensure data consistency, then data anomalies are prevented, but resource consumption increases
Solution Approach 1:
The workflow execution is divided into discrete tasks with明确的 data dependency relationships. When data changes are detected, the system segments the re-execution scope to include only those tasks that depend on the changed data, as determined by analyzing data dependency metadata. This prevents unnecessary resource consumption from re-executing unrelated tasks while still ensuring data consistency for affected operations.
Solution Approach 2:
The system implements a feedback mechanism that monitors data changes during workflow execution. When changes are detected, the feedback loop triggers identification of affected tasks through data dependency analysis, and initiates selective re-execution. This feedback-driven approach ensures data consistency is maintained for changed data while optimizing resource consumption by avoiding redundant task executions.
3Adaptability or versatility
If data updates are allowed during workflow execution, then workflow adaptability improves, but system complexity increases
Solution Approach 1:
Data dependency relationships and task metadata are pre-analyzed and stored before workflow execution begins. This preliminary action creates a dependency map that enables rapid identification of affected tasks when data changes occur, without requiring complex real-time analysis during workflow execution. The pre-computed dependency information simplifies the runtime decision-making process while maintaining high adaptability.
Solution Approach 2:
The system introduces an intermediary layer consisting of data dependency metadata and task labels that mediate between data changes and workflow task execution. This intermediary structure allows the system to handle data updates adaptably by translating data changes into identified affected tasks through the dependency metadata, without directly complicating the core workflow execution logic.
4Use of energy by moving object
If selective re-execution of tasks is implemented, then resource consumption is reduced, but implementation complexity increases
Solution Approach 1:
Task labels and data dependency metadata are pre-assigned to each workflow task during workflow design and registration. This preliminary action creates a ready-to-use dependency map that enables efficient selective re-execution without requiring complex analysis algorithms during runtime. The pre-computed information simplifies the implementation of selective re-execution by providing direct lookup capabilities for identifying affected tasks.
Solution Approach 2:
The system uses copies of task metadata and dependency information stored in a readily accessible format. Instead of performing complex real-time analysis of task dependencies, the system references pre-stored copies of dependency relationships and task attributes. This copying approach reduces implementation complexity by replacing complex computational logic with simple metadata queries and comparisons.
Data Source
AI summary
A computer-implemented smart recovery system for dynamic workflows addresses a change to a data object during execution of an instance of a workflow by selectively re-executing workflow tasks that are affected by the change, without cancelling the instance and restarting a new instance of the workflow. A determination of whether a task is to be re-executed during the smart recovery process may include examining a re-evaluation label assigned to the task.