INFERRING INFORMATION ABOUT CORRESPONDENCES BETWEEN DATA SOURCES FOR DATASPACES

Abstract

Traditional data integration offers high quality services for managing and querying interrelated but heterogeneous data sources but at a high cost. This is because a significant amount of manual effort is required to help specify precise relationships between the data sources in order to set up a data integration system. The recent proposed vision of dataspaces aims to reduce the upfront effort required to set up the system. A possible solution to approaching this aim is to infer schematic correspondences between the data sources, thus enabling the development of automated means for bootstrapping dataspaces. In this thesis, we discuss a two-step research programme to automatically infer schematic correspondences between data sources. In the first step, we investigate the effectiveness of existing schema matching approaches for inferring schematic correspondences and contribute a benchmark, called MatchBench, to achieve this aim. In the second step, we contribute an evolutionary search method to identify the set of entity-level relationships (ELRs) between data sources that qualify as entity-level schematic correspondences. Specifically, we model the requirements using a vector space model. For each resulting ELR we further identify a set of attribute-level relationships (ALRs) that qualify as attribute-level schematic correspondences. We demonstrate the effectiveness of the contributed inference technique using both MatchBench scenarios and real world scenarios.

Date of Award	31 Jul 2012
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Alvaro Fernandes (Supervisor)