Abstract
Model matching is a fundamental operation for various model management aspects such as model retrieval, evolution, and merging. An accurate matching between the elements of the matched models results in a better model management. This paper presents a Hybridized Greedy-Genetic algorithm for matching UML class diagrams, considering their lexical, internal, and structural similarity. Additionally, using a case study of five class diagrams, the performance of the Hybridized algorithm is empirically compared against the traditional Genetic algorithm in terms of both matching accuracy and convergence time.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 161-166 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538616383 |
| DOIs | |
| State | Published - 6 Nov 2017 |
Publication series
| Name | Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017 |
|---|---|
| Volume | 1 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Hybridized-Greedy-Genetic
- model matching
- similarity metrics
ASJC Scopus subject areas
- Information Systems and Management
- Management Science and Operations Research
- Strategy and Management
- Computer Science Applications