Conference Proceeding

Schema merging and mapping creation for relational sources.

01/2008; DOI:10.1145/1353343.1353357 In proceeding of: EDBT 2008, 11th International Conference on Extending Database Technology, Nantes, France, March 25-29, 2008, Proceedings
Source: DBLP

ABSTRACT We address the problem of generating a mediated schema from a set of relational data source schemas and conjunctive queries that specify where those schemas overlap. Unlike past approaches that generate only the mediated schema, our algorithm also generates view definitions , i.e., source-to-mediated schema mappings. Our main goal is to understand the requirements that a mediated schema and views should satisfy, such as completeness, preserva- tion of overlapping information, normalization, and minimality. We show how these requirements influence the detailed structure of schemas and view definitions that are produced. We introduce a normal form for mediated schemas and view definitions, show how to generate them, and prove that schemas and views in this normal form satisfy our requirements. The view definitions in our normal form use stylized GLAV mappings, for which query rewriting is easier than general GLAV mappings. We demonstrate the efficiency of query rewriting in a prototype implementation.

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    ABSTRACT: Merging schemas or other structured data occur in many different data models and applications, including merging ontologies, view integration, data integration, and computer supported collaborative work. This paper describes some of the key works in merging schemas and discusses some of the commonalities and differences.
    12/2010: pages 223-249;
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    ABSTRACT: Mediated schemas lie at the center of the well recognized data integration architecture. Classical data integration systems rely on a mediated schema created by human experts through an intensive design process. Automatic generation of mediated schemas is still a goal to be achieved. We generate mediated schemas by merging multiple source schemas interrelated by tuple-generating dependencies (tgds). Schema merging is the process to consolidate multiple schemas into a unified view. The task becomes particularly challenging when the schemas are highly heterogeneous and autonomous. Existing approaches fall short in various aspects, such as restricted expressiveness of input mappings, lacking data level interpretation, the output mapping is not in a logical language (or not given at all), and being confined to binary merging. We present here a novel system which is able to perform native n-ary schema merging using P2P style tgds as input. Suited in the scenario of generating mediated schemas for data integration, the system opts for a minimal schema signature retaining all certain answers of conjunctive queries. Logical output mappings are generated to support the mediated schemas, which enable query answering and, in some cases, query rewriting.
    Proceedings of the 27th International Conference on Data Engineering, ICDE 2011, April 11-16, 2011, Hannover, Germany; 01/2011
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    ABSTRACT: The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies to provide a unified view on them. We demonstrate a new automatic approach to merge large taxonomies such as product catalogs or web directories. Our approach is based on an equivalence matching between a source and target taxonomy to merge them. It is target-driven, i.e. it preserves the structure of the target taxonomy as much as possible. Further, we show how the approach can utilize additional relationships between source and target concepts to semantically improve the merge result.
    Data Engineering (ICDE), 2011 IEEE 27th International Conference on; 05/2011

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