Library of American University of Madaba
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Bellahsene, Zohra.
Schema Matching and Mapping [electronic resource] / edited by Zohra Bellahsene, Angela Bonifati, Erhard Rahm. - XII, 320 p. online resource. - Data-Centric Systems and Applications .
Part I: Large-scale and knowledge-driven schema matching. 1. Towards large-scale schema and ontology matching. 2. Interactive techniques to support ontology matching. 3. Enhancing the capabilities of attribute correspondences. 4. Uncertainty in data integration and dataspace support platforms -- Part II: Quality-driven schema mapping and evolution. 5. Discovery and correctness of schema mapping transformations. 6. Recent advances in schema and ontology evolution. 7. Schema mapping evolution through composition and inversion. 8. Mapping-based merging of schemas -- Part III: Evaluating and tuning of matching tasks. 9. On evaluating schema matching and mapping. 10. Tuning for schema matching.
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.
9783642165184
Computer science.
Database management.
Artificial intelligence.
Computer Science.
Database Management.
Mathematical Logic and Formal Languages.
Artificial Intelligence (incl. Robotics).
QA76.9.D3
005.74
Schema Matching and Mapping [electronic resource] / edited by Zohra Bellahsene, Angela Bonifati, Erhard Rahm. - XII, 320 p. online resource. - Data-Centric Systems and Applications .
Part I: Large-scale and knowledge-driven schema matching. 1. Towards large-scale schema and ontology matching. 2. Interactive techniques to support ontology matching. 3. Enhancing the capabilities of attribute correspondences. 4. Uncertainty in data integration and dataspace support platforms -- Part II: Quality-driven schema mapping and evolution. 5. Discovery and correctness of schema mapping transformations. 6. Recent advances in schema and ontology evolution. 7. Schema mapping evolution through composition and inversion. 8. Mapping-based merging of schemas -- Part III: Evaluating and tuning of matching tasks. 9. On evaluating schema matching and mapping. 10. Tuning for schema matching.
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.
9783642165184
Computer science.
Database management.
Artificial intelligence.
Computer Science.
Database Management.
Mathematical Logic and Formal Languages.
Artificial Intelligence (incl. Robotics).
QA76.9.D3
005.74