There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

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A survey of approaches to automatic schema matching

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

A federated database system (FDBS) is a collection of cooperating database systems that are autonomous and possibly heterogeneous. In this paper, we define a reference architecture for distributed database management systems from system and schema viewpoints and show how various FDBS architectures can be developed. We then define a methodology for developing one of the popular architectures of an FDBS. Finally, we discuss critical issues related to developing and operating an FDBS.

Federated database systems for managing distributed, heterogeneous, and autonomous databases

From the Publisher:
Fundamentals of Database Systems combines clear explanations of theory and design, broad coverage of models and real systems, and excellent examples with up-to-date introductions to modern database technologies. This edition is completely revised and updated, and reflects the latest trends in technological and application development. Professors Elmasri and Navathe focus on the relational model and include coverage of recent object-oriented developments. They also address advanced modeling and system enhancements in the areas of active databases, temporal and spatial databases, and multimedia information systems. This edition also surveys the latest application areas of data warehousing, data mining, web databases, digital libraries, GIS, and genome databases. New to the Third Edition
Reorganized material on data modeling to clearly separate entity relationship modeling, extended entity relationship modeling, and object-oriented modeling Expanded coverage of the object-oriented and object/relational approach to data management, including ODMG and SQL3 Uses examples from real database systems including OracleTM and Microsoft AccessAE Includes discussion of decision support applications of data warehousing and data mining, as well as emerging technologies of web databases, multimedia, and mobile databases Covers advanced modeling in the areas of active, temporal, and spatial databases Provides coverage of issues of physical database tuning Discusses current database application areas of GIS, genome, and digital libraries

Fundamentals of Database Systems

Fundamentals of Database Systems, 5th Edition

The authors present a common foundation for integrating pairs of entity sets, pairs of relationship sets, and an entity set with a relationship set. This common foundation is based on the basic principle of integrating attributes. Any pair of objects whose identifying attributes can be integrated can themselves be integrated. Several definitions of attribute equivalence are presented. These definitions can be used to specify the exact nature of the relationship between a pair of attributes. Based on these definitions, several strategies for attribute integration are presented and evaluated. >

A theory of attributed equivalence in databases with application to schema integration

Fundamentals of database systems (2nd ed.)

This document1 contains definitions of a wide range of concepts specific to and widely used within temporal databases. In addition to providing definitions, the document also includes explanations of concepts as well as discussions of the adopted names.

Ramez Elmasri

Papers

Fundamentals of Database Systems

Fundamentals of Database Systems, 5th Edition

A theory of attributed equivalence in databases with application to schema integration

Fundamentals of database systems (2nd ed.)

The Consensus Glossary of Temporal Database Concepts - February 1998 Version.