Schema Matching and Mapping (Data-Centric Systems and Applications)

Requiring heterogeneous information systems to cooperate and communicate has now become crucial, especially in application areas like e-business, Web-based mash-ups and the life sciences. Such cooperating systems have to automatically and efficiently match, exchange, transform and integrate large data sets from different sources and of different structure in order to enable seamless data exchange and transformation.

The book edited by Bellahsene, Bonifati and Rahm provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed the above requirements and points to the open technical challenges. The contributions from leading experts are structured into three parts: large-scale and knowledge-driven schema matching, quality-driven schema mapping and evolution, and evaluation and tuning of matching tasks. The authors describe the state of the art by discussing the latest achievements such as more effective methods for matching data, mapping transformation verification, adaptation to the context and size of the matching and mapping tasks, mapping-driven schema evolution and merging, and mapping evaluation and tuning. The overall result is a coherent, comprehensive picture of the field.

With this book, the editors introduce graduate students and advanced professionals to this exciting field. For researchers, they provide an up-to-date source of reference about schema and ontology matching, schema and ontology evolution, and schema merging.

Requiring heterogeneous information systems to cooperate and communicate has now become crucial, especially in application areas like e-business, Web-based mash-ups and the life sciences. Such cooperating systems have to automatically and efficiently match, exchange, transform and integrate large data sets from different sources and of different structure in order to enable seamless data exchange and transformation.

The book edited by Bellahsene, Bonifati and Rahm provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed the above requirements and points to the open technical challenges. The contributions from leading experts are structured into three parts: large-scale and knowledge-driven schema matching, quality-driven schema mapping and evolution, and evaluation and tuning of matching tasks. The authors describe the state of the art by discussing the latest achievements such as more effective methods for matching data, mapping transformation verification, adaptation to the context and size of the matching and mapping tasks, mapping-driven schema evolution and merging, and mapping evaluation and tuning. The overall result is a coherent, comprehensive picture of the field.

With this book, the editors introduce graduate students and advanced professionals to this exciting field. For researchers, they provide an up-to-date source of reference about schema and ontology matching, schema and ontology evolution, and schema merging.