Hierarchical clustering of metamodels for comparative analysis and visualization

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

9 Citaties (Scopus)

Uittreksel

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.
TaalEngels
TitelModelling Foundations and Applications
Subtitel12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings
RedacteurenA. Wąsowski, H. Loenn
Plaats van productieDordrecht
UitgeverijSpringer
Pagina's3-18
ISBN van elektronische versie978-3-319-42061-5
ISBN van geprinte versie978-3-319-42060-8
DOI's
StatusGepubliceerd - 2016

Publicatie series

NaamLecture Notes in Computer Science
UitgeverijSpringer
Volume9764

Vingerafdruk

Visualization
Recovery
Vector spaces
Merging
Processing

Citeer dit

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 (editors), Modelling Foundations and Applications : 12th European Conference, ECMFA 2016, Held as Part of STAF 2016, Vienna, Austria, July 6-7, 2016, Proceedings (blz. 3-18). (Lecture Notes in Computer Science; Vol. 9764). Dordrecht: Springer. DOI: 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. redacteur / A. Wąsowski ; H. Loenn. Dordrecht : Springer, 2016. blz. 3-18 (Lecture Notes in Computer Science).
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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.",
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Babur, Ö, Cleophas, LGWA & van den Brand, MGJ 2016, Hierarchical clustering of metamodels for comparative analysis and visualization. in A Wąsowski & H Loenn (redactie), 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, blz. 3-18. DOI: 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. redactie / A. Wąsowski; H. Loenn. Dordrecht : Springer, 2016. blz. 3-18 (Lecture Notes in Computer Science; Vol. 9764).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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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.

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Babur Ö, Cleophas LGWA, van den Brand MGJ. Hierarchical clustering of metamodels for comparative analysis and visualization. In Wąsowski A, Loenn H, redacteurs, 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. blz. 3-18. (Lecture Notes in Computer Science). Beschikbaar vanaf, DOI: 10.1007/978-3-319-42061-5_1