Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm

Mojeeb Al Rhman Al-Khiaty; Moataz Ahmed

doi:10.1109/STC-CSIT.2017.8098759

Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

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	https://doi.org/10.1109/STC-CSIT.2017.8098759
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

Access to Document

10.1109/STC-CSIT.2017.8098759

Cite this

Al-Khiaty, M. A. R., & Ahmed, M. (2017). Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm. In Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017 (pp. 161-166). Article 8098759 (Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/STC-CSIT.2017.8098759

Al-Khiaty, Mojeeb Al Rhman ; Ahmed, Moataz. / Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm. Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 161-166 (Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017).

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Al-Khiaty, MAR & Ahmed, M 2017, Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm. in Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017., 8098759, Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017, vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 161-166. https://doi.org/10.1109/STC-CSIT.2017.8098759

Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm. / Al-Khiaty, Mojeeb Al Rhman ; Ahmed, Moataz.
Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 161-166 8098759 (Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Al-Khiaty MAR , Ahmed M. Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm. In Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 161-166. 8098759. (Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017). doi: 10.1109/STC-CSIT.2017.8098759

Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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