Clustering Process Models Using Distance Matrices

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Manual search for matching process models in large databases is time-consuming due to the variety of process models in different languages, making it difficult for users to efficiently analyze and implement processes.

Innovation Solution

A method and apparatus for automatically clustering process models using a distance matrix calculated based on natural language grammar and process modeling languages, employing multiple calculation levels to determine dissimilarity, and partitioning models into clusters using a medoid-based clustering algorithm.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual search is used to find matching process models in a large database, then the user can evaluate and compare process models, but the search process becomes very time-consuming

Engineering Contradiction:

Improveaccuracy of finding matching process modelsVSAvoidtime required for manual search

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs automatic clustering of process models without requiring manual user intervention. The clustering algorithm autonomously analyzes process model characteristics, calculates distance matrices, and groups similar models together, enabling the system to serve itself in finding matching models rather than relying on manual user search

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical search process is replaced by an automated computational system. The system uses distance matrix calculations based on process model characteristics and clustering algorithms to automatically identify and group similar process models, substituting the manual mechanical browsing and comparison process with an automated information processing system

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If process models from different process modeling languages are stored in the database, then the database contains diverse process models for various applications, but the variety of languages makes manual search and comparison difficult

Engineering Contradiction:

Improvesupport for multiple process modeling languagesVSAvoidease of searching and comparing process models

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The clustering system is designed to handle multiple process modeling languages (UML, EPC, Petri nets, etc.) through a universal distance calculation approach. The system extracts characteristics from different language formats and applies unified clustering algorithms, making the system multi-functional in processing various process model types while maintaining ease of operation

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system transforms process models from different languages into a unified parameter representation through distance matrix calculations. By changing the representation parameters to a common format based on process model characteristics, the system enables easy comparison and clustering of diverse process models regardless of their original language format

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS8180627B2Method and an apparatus for clustering process models

Publication Date: 2012.05.15 SIEMENS AG

AI summary

The invention relates to an apparatus for clustering process models each consisting of model elements comprising a text phrase which describes in a natural language a process activity according to a process modeling language grammar and a natural language grammar, wherein said apparatus comprises a process object ontology memory for storing a process object ontology, a distance calculation unit for calculating a distance matrix employing said processing modeling language grammar and said natural language grammar, wherein said distance matrix consists of distances each indicating a dissimilarity of a pair of said process models, and a clustering unit which partitions said process models into a set of clusters based on said calculated distance matrix.