Avigdor Gal, "Uncertain Schema Matching"
Mo rgan & Cla ypool Pu blishers | 2011 | ISBN: 1608454339 | 100 pages | PDF | 1,7 MB
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 of uncertainty 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