Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. Schema matching is one of the basic operations required by the process of data and schema integration, and thus has a great effect on its outcomes, whether these involve targeted content delivery, view integration, database integration, query rewriting over heterogeneous sources, duplicate data elimination, or automatic streamlining of workflow activities that involve heterogeneous data sources. Although schema matching research has been…mehr
Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. Schema matching is one of the basic operations required by the process of data and schema integration, and thus has a great effect on its outcomes, whether these involve targeted content delivery, view integration, database integration, query rewriting over heterogeneous sources, duplicate data elimination, or automatic streamlining of workflow activities that involve heterogeneous data sources. Although schema matching research has been ongoing for over 25 years, more recently a realization has emerged that schema matchers are inherently uncertain. Since 2003, work on the uncertainty in schema matching has picked up, along with research on uncertainty in other areas of data management. This lecture presents various aspects of uncertainty in schema matching within a single unified framework. We introduce basic formulations ofuncertainty and provide several alternative representations of schema matching uncertainty. Then, we cover two common methods that have been proposed to deal with uncertainty in schema matching, namely ensembles, and top-K matchings, and analyze them in this context. We conclude with a set of real-world applications. Table of Contents: Introduction / Models of Uncertainty / Modeling Uncertain Schema Matching / Schema Matcher Ensembles / Top-K Schema Matchings / Applications / Conclusions and Future Work
Avigdor Gal is an Associate Professor at the Faculty of Industrial En[1]gineering & Management at the Technion - Israel Institute of Tech[1]nology. He received his D.Sc. degree from the Technion in 1995 in the area of temporal active databases. He has published more than 90 papers in journals (e.g., Journal of the ACM ( JACM), ACM Transac[1]tions on Database Systems (TODS), IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Tech[1]nology (TOIT), and the VLDB Journal), books (Temporal Databases: Research and Practice, Schema Matching and Mapping, and Reasoning in Event-based Distributed Systems) and conferences (ICDE, ER, CoopIS, BPM, DEBS) on the topics of data integration, complex event processing, temporal databases, information systems architectures, and active databases. Avigdor is a member of CoopIS (Cooperative Information Systems) Advisory Board, a member of IFIP WG 2.6, and a recipient of the IBM Faculty Award for 2002-2004.He is a member of the ACM and a senior member of IEEE. Avigdor served as a Program co-Chair of CoopIS and DEBS, and in various roles in ER and CIKM. He served as a program committee member in SIGMOD, VLDB, ICDE and others. Avigdor was an Area Editor of the Encyclopedia of Database Systems.
Inhaltsangabe
Introduction.- Models of Uncertainty.- Modeling Uncertain Schema Matching.- Schema Matcher Ensembles.- Top-K Schema Matchings.- Applications.- Conclusions and Future Work.
