Process Model Validation via Sub-Model Segmentation
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Solution Overview
Problem
Existing process model validation tools are inefficient, often requiring extensive processing time, which can lead to errors and the risk of incorrect process execution due to potential misunderstandings between process and computer domain experts, and may cause designers to skip validation, thereby delaying the availability of business processes.
Innovation Solution
A computer system that validates process models by identifying executable and non-executable portions of the model by propagating variables and splitting the model into sub-models to check dependencies and pre-conditions, allowing for validation within a reduced set of initial states, thereby reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete model validation is performed to ensure correctness, then reliability of process execution is improved, but processing time increases significantly
Solution Approach 1:
The patent divides the process model into multiple sub-models based on the control flow structure. Each sub-model represents a specific portion of the overall process and can be validated independently. This segmentation allows the validation system to process only relevant sub-models rather than the entire model, significantly reducing validation time while maintaining comprehensive coverage of all process steps.
Solution Approach 2:
The patent performs preliminary analysis to identify the entry points and scope of validation before executing the actual validation. By determining which sub-models need to be validated based on the current process state and entry points, the system avoids unnecessary validation of unrelated portions of the model, thus reducing processing time while ensuring all necessary validations are performed.
2Productivity
If validation is skipped to reduce processing time, then productivity is improved, but reliability deteriorates due to undetected errors
Solution Approach 1:
By segmenting the validation process into independent sub-model validations, the system can quickly validate only the necessary portions of the process model. This selective validation approach maintains error detection capability for relevant process steps while avoiding the time-consuming validation of the entire model, thus preserving both productivity and reliability.
Solution Approach 2:
The patent implements partial validation by validating only the necessary sub-models based on entry points and process state, rather than performing complete validation of all possible paths. This partial action approach provides sufficient error detection for the current execution context while significantly reducing validation time, achieving a balance between productivity and reliability.
3Measurement precision
If detailed validation of all process paths is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent simplifies the validation system complexity by segmenting the process model into sub-models and validating them independently. This segmentation reduces the computational complexity of the validation system while maintaining thorough validation of each sub-model, as the system only needs to manage and validate smaller, manageable units rather than the entire complex model at once.
Data Source
AI summary
In computer-implemented methods to validate process models, the models carry semantic information that is related to the process steps, including pre-conditions that indicate whether a step would be executable under certain conditions and post-conditions relating to results from executing a step. Steps are related as predecessor/successor so that a post-condition of the predecessor step can be the pre-condition of a successor step. During model validation, variables are exchanged and propagated between the step conditions. Instead of validating the model for potentially all initial values of variables and conditions, the system identifies portions of the model that can be validated separately for a reduced set of initial states.