Process Model Benchmarking Using Structural Complexity Metrics
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Solution Overview
Problem
The analysis of enterprise-level software processes is computationally expensive and resource-intensive due to the complexity of process mining algorithms and the need to compare processes across organizations, requiring significant time and effort for data preprocessing and computational resources.
Innovation Solution
A method for benchmarking process models by evaluating high-level features such as the number of elements, roles, and handovers, using computer-implemented functions to calculate metrics and compare them with reference values, reducing the focus on specific tasks or data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If automated process discovery techniques are used to analyze business processes, then process analysis capability is improved, but computational resources and time requirements increase significantly
Solution Approach 1:
The patent extracts only the essential structural features of process models (number of elements, roles, handovers) rather than analyzing complete event logs and transaction data. This extraction approach maintains process analysis capability while significantly reducing computational resource requirements by focusing on high-level model characteristics instead of detailed operational data.
Solution Approach 2:
The patent segments the process model into distinct measurable features (elements, roles, handovers) that can be evaluated independently. This segmentation allows the system to analyze process models through multiple discrete metrics rather than requiring comprehensive computational analysis of the entire process data, thereby reducing overall computational burden.
2Measurement precision
If process models are evaluated in detail to provide comprehensive insights, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by evaluating different aspects of process models with different levels of detail. Instead of uniformly analyzing every aspect of each model with maximum precision, the system selectively measures specific features (elements, roles, handovers) at appropriate levels of granularity, providing comprehensive insights while managing system complexity through differentiated evaluation approaches.
Data Source
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AI summary
The present disclosure provides techniques and solutions for benchmarking process models by evaluating characteristics of the model, such as those reflecting model complexity. Metrics can include the number of elements in a model, the number of roles, and the number of handoffs between roles, as a few examples. Metrics for a model can be compared with reference metrics, such as those calculated from a set of other models, which can be for the same modeled process or different processes. Collections of process models can be evaluated in a similar manner, including for a set of related models that may be expressed at different levels of specificity. Metrics for individual models in the collection can be evaluated and aggregated, and then compared with aggregated metric values of other model collections, for the same or different modeled processes.