Hierarchical clustering of metamodels for comparative analysis and visualization

Ö. Babur, L.G.W.A. Cleophas, M.G.J. van den Brand, A. Wąsowski (Editor), H. Loenn (Editor)

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

23 Citations (Scopus)

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
Place of PublicationDordrecht
PublisherSpringer
Pages3-18
Number of pages16
ISBN (Print)9783319420608
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventEuropean Conference on Modelling Foundations and Applications: Held as Part of STAF 2016 - , Austria
Duration: 6 Jul 20167 Jul 2016
Conference number: 12

Publication series

Name ECMFA: European Conference on Modelling Foundations and Applications
PublisherSpringer

Conference

ConferenceEuropean Conference on Modelling Foundations and Applications
Abbreviated titleECMFA
Country/TerritoryAustria
Period6/07/167/07/16

Keywords

  • Hierarchical clustering
  • Model comparison
  • Model-driven engineering
  • R
  • Vector space model

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