Dipl.-Inf. Konrad Voigt

Structural Graph-based Metamodel Matching

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Kurzfassung in Englisch

Data integration has been, and still is, a challenge for applications processing multiple heterogeneous data sources. Across the domains of schemas, ontologies, and metamodels, this imposes the need for mapping specifications, i.e. the task of discovering semantic correspondences between elements. Support for the development of such mappings has been researched, producing matching systems that automatically propose mapping suggestions.

However, especially in the context of metamodel matching the result quality of state of the art matching techniques leaves room for improvement. Although the traditional approach of pair-wise element comparison works on smaller data sets, its quadratic complexity leads to poor runtime and memory performance and eventually to the inability to match, when applied on real-world data.

The work presented in this thesis seeks to address these shortcomings. Thereby, we take advantage of the graph structure of metamodels. Consequently, we derive a planar graph edit distance as metamodel similarity metric and mining-based matching to make use of redundant information. We also propose a planar graph-based partitioning to cope with large-scale matching. These techniques are then evaluated using real-world mappings from SAP business integration scenarios and the MDA community. The results demonstrate improvement in quality and managed runtime and memory consumption for large-scale metamodel matching.

weitere Metadaten

Metamodell, Ähnlichkeit, Graphen, Planar
Metamodel, Matching, Graph, Planar
DDC Klassifikation004
RVK KlassifikationST 274, ST 230
HochschuleTechnische Universität Dresden
FakultätFakultät Informatik
ProfessurProfessur für Softwaretechnologie
BetreuerProf. Dr. rer. nat. habil. Uwe Aßmann
GutachterProf. Dr. rer. nat. habil. Uwe Aßmann
Prof. Dr. Jorge Cardoso
Tag d. Einreichung (bei der Fakultät)15.07.2011
Tag d. Verteidigung / Kolloquiums / Prüfung02.11.2011
Veröffentlichungsdatum (online)17.01.2012
persistente URNurn:nbn:de:bsz:14-qucosa-81671

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