Hierarchical clustering of metamodels for comparative analysis and visualization

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)
5 Downloads (Pure)

Abstract

Many applications in Model-Driven Engineering involve processing multiple models or metamodels. A good example is the comparison and merging of metamodel variants into a common metamodel in domain model recovery. Although there are many sophisticated techniques to process the input dataset, little attention has been given to the initial data analysis, visualization and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. In this paper we present a generic approach for metamodel comparison, analysis and visualization as an exploratory first step for domain model recovery. We propose representing metamodels in a vector space model, and applying hierarchical clustering techniques to compare and visualize them as a tree structure. We demonstrate our approach on two Ecore datasets: a collection of 50 state machine metamodels extracted from GitHub as top search results; and ∼

100 metamodels from 16 different domains, obtained from AtlanMod Metamodel Zoo.
Original languageEnglish
Title of host publicationModelling Foundations and Applications
Subtitle of host publication12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings
EditorsA. Wąsowski, H. Loenn
Place of PublicationDordrecht
PublisherSpringer
Pages3-18
ISBN (Electronic)978-3-319-42061-5
ISBN (Print)978-3-319-42060-8
DOIs
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9764

Fingerprint

Visualization
Recovery
Vector spaces
Merging
Processing

Cite this

Babur, Ö., Cleophas, L. G. W. A., & van den Brand, M. G. J. (2016). Hierarchical clustering of metamodels for comparative analysis and visualization. In A. Wąsowski, & H. Loenn (Eds.), Modelling Foundations and Applications : 12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings (pp. 3-18). (Lecture Notes in Computer Science; Vol. 9764). Dordrecht: Springer. https://doi.org/10.1007/978-3-319-42061-5_1
Babur, Ö. ; Cleophas, L.G.W.A. ; van den Brand, M.G.J. / Hierarchical clustering of metamodels for comparative analysis and visualization. Modelling Foundations and Applications : 12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings. editor / A. Wąsowski ; H. Loenn. Dordrecht : Springer, 2016. pp. 3-18 (Lecture Notes in Computer Science).
@inproceedings{7007684b07544206a5feddef5d749dc4,
title = "Hierarchical clustering of metamodels for comparative analysis and visualization",
abstract = "Many applications in Model-Driven Engineering involve processing multiple models or metamodels. A good example is the comparison and merging of metamodel variants into a common metamodel in domain model recovery. Although there are many sophisticated techniques to process the input dataset, little attention has been given to the initial data analysis, visualization and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. In this paper we present a generic approach for metamodel comparison, analysis and visualization as an exploratory first step for domain model recovery. We propose representing metamodels in a vector space model, and applying hierarchical clustering techniques to compare and visualize them as a tree structure. We demonstrate our approach on two Ecore datasets: a collection of 50 state machine metamodels extracted from GitHub as top search results; and ∼ ∼100 metamodels from 16 different domains, obtained from AtlanMod Metamodel Zoo.",
author = "{\"O}. Babur and L.G.W.A. Cleophas and {van den Brand}, M.G.J.",
year = "2016",
doi = "10.1007/978-3-319-42061-5_1",
language = "English",
isbn = "978-3-319-42060-8",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "3--18",
editor = "A. Wąsowski and H. Loenn",
booktitle = "Modelling Foundations and Applications",
address = "Germany",

}

Babur, Ö, Cleophas, LGWA & van den Brand, MGJ 2016, Hierarchical clustering of metamodels for comparative analysis and visualization. in A Wąsowski & H Loenn (eds), Modelling Foundations and Applications : 12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings. Lecture Notes in Computer Science, vol. 9764, Springer, Dordrecht, pp. 3-18. https://doi.org/10.1007/978-3-319-42061-5_1

Hierarchical clustering of metamodels for comparative analysis and visualization. / Babur, Ö.; Cleophas, L.G.W.A.; van den Brand, M.G.J.

Modelling Foundations and Applications : 12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings. ed. / A. Wąsowski; H. Loenn. Dordrecht : Springer, 2016. p. 3-18 (Lecture Notes in Computer Science; Vol. 9764).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Hierarchical clustering of metamodels for comparative analysis and visualization

AU - Babur, Ö.

AU - Cleophas, L.G.W.A.

AU - van den Brand, M.G.J.

PY - 2016

Y1 - 2016

N2 - Many applications in Model-Driven Engineering involve processing multiple models or metamodels. A good example is the comparison and merging of metamodel variants into a common metamodel in domain model recovery. Although there are many sophisticated techniques to process the input dataset, little attention has been given to the initial data analysis, visualization and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. In this paper we present a generic approach for metamodel comparison, analysis and visualization as an exploratory first step for domain model recovery. We propose representing metamodels in a vector space model, and applying hierarchical clustering techniques to compare and visualize them as a tree structure. We demonstrate our approach on two Ecore datasets: a collection of 50 state machine metamodels extracted from GitHub as top search results; and ∼ ∼100 metamodels from 16 different domains, obtained from AtlanMod Metamodel Zoo.

AB - Many applications in Model-Driven Engineering involve processing multiple models or metamodels. A good example is the comparison and merging of metamodel variants into a common metamodel in domain model recovery. Although there are many sophisticated techniques to process the input dataset, little attention has been given to the initial data analysis, visualization and filtering activities. These are hard to ignore especially in the case of a large dataset, possibly with outliers and sub-groupings. In this paper we present a generic approach for metamodel comparison, analysis and visualization as an exploratory first step for domain model recovery. We propose representing metamodels in a vector space model, and applying hierarchical clustering techniques to compare and visualize them as a tree structure. We demonstrate our approach on two Ecore datasets: a collection of 50 state machine metamodels extracted from GitHub as top search results; and ∼ ∼100 metamodels from 16 different domains, obtained from AtlanMod Metamodel Zoo.

U2 - 10.1007/978-3-319-42061-5_1

DO - 10.1007/978-3-319-42061-5_1

M3 - Conference contribution

SN - 978-3-319-42060-8

T3 - Lecture Notes in Computer Science

SP - 3

EP - 18

BT - Modelling Foundations and Applications

A2 - Wąsowski, A.

A2 - Loenn, H.

PB - Springer

CY - Dordrecht

ER -

Babur Ö, Cleophas LGWA, van den Brand MGJ. Hierarchical clustering of metamodels for comparative analysis and visualization. In Wąsowski A, Loenn H, editors, Modelling Foundations and Applications : 12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings. Dordrecht: Springer. 2016. p. 3-18. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-42061-5_1