Structural Graph-based Metamodel Matching

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

an algebraic framework for schema matching

DatabaseSoc China Comp Federat, Natl Sci Fdn China, Zhejiang Univ, Y C Tang Disciplinary Dev & Fund, Oracle ChinaIt is well known that a formal framework for the schema matching problem (SMP) is important because it facilitates the building of algorithm model and the evaluation of algorithms. First, based on universal algebra, we propose a meta-meta str

schema homomorphism - an algebraic framework for schema matching

AIT, INRIA, UNU/IISTA formal framework for SMP is important because it facilitates the building of algorithm model and the evaluation of algorithms. First, we propose a formal definition of schema matching that is named multivalent matching, i.e., an individual