Automated Common Modeling Pattern Mining for Business Process Models
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Solution Overview
Problem
Current methods lack automation in identifying and providing common modeling patterns for business processes, relying heavily on manual evaluation by experts, which is inefficient and may produce unreliable results.
Innovation Solution
A method that automatically establishes linkages between process elements and reference model elements, extracts elementary process patterns, and constructs a process goal tree to detect frequently occurring patterns, thereby identifying common modeling patterns across multiple process models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation by experts is used to identify common modeling patterns, then reliability of pattern identification may be maintained through expert judgment, but productivity is reduced due to inefficiency and time consumption
Solution Approach 1:
The system performs automated pattern identification without requiring expert intervention. The apparatus automatically extracts process models, compares them, and identifies common patterns through algorithmic analysis, allowing the system to serve itself rather than relying on external expert evaluation.
Solution Approach 2:
The patent replaces the mechanical process of manual expert evaluation with an automated computational system. The apparatus uses computer-based algorithms to perform pattern recognition and comparison, substituting human expert mechanics with automated digital processing while maintaining reliability through systematic analysis.
2Adaptability or versatility
If manual evaluation methods are used for pattern identification, then flexibility in handling complex cases is preserved, but loss of time occurs due to the manual process
Solution Approach 1:
The automated system handles complex pattern identification cases independently without requiring manual intervention. The apparatus processes complex process models through automated comparison algorithms, maintaining adaptability while eliminating time loss associated with manual evaluation.
Solution Approach 2:
The system performs preliminary automated analysis of process models to identify patterns before any manual review might be needed. By pre-processing and comparing models automatically, the system reduces the time required while maintaining the ability to handle complexity through systematic analysis.
3Productivity
If automated methods are implemented for pattern identification, then productivity is improved through efficiency, but reliability may deteriorate due to lack of expert judgment
Solution Approach 1:
The patent replaces manual expert mechanics with automated computational mechanics. The apparatus uses systematic algorithmic processes to identify patterns, achieving high productivity through automation while maintaining reliability through consistent, error-free computational analysis that can be validated and reproduced.
Solution Approach 2:
The automated system incorporates feedback mechanisms to validate and refine pattern identification results. The apparatus can compare identified patterns against established criteria, verify consistency across multiple process models, and adjust its analysis based on feedback from the comparison results, thereby maintaining reliability without expert intervention.
4Adaptability or versatility
If formal representation of process patterns is implemented, then adaptability is improved for automated retrieval and reasoning, but device complexity increases due to the need for formal structures
Solution Approach 1:
The patent implements a universal formal representation structure that serves multiple functions: it enables automated retrieval, supports reasoning operations, and provides a standardized format for pattern storage. This multi-functional approach increases adaptability while managing complexity through a single versatile formal framework rather than multiple specialized structures.
Data Source
AI summary
At least one embodiment of the present invention is directed to a method and/or an apparatus for automatically providing a common modelling pattern as a function of a plurality of stored process models. The common modelling patterns are identified according to three substeps, namely semantic annotation, extraction of pattern based description and composite process pattern mining. The detected common modelling patterns serve as best practice candidates as regards process engineering. At least one embodiment of the present invention finds application in a variety of domains being related to process management, such as process design, process mining and semantic process planning.