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

Data models can provide powerful abstractions to aid in the design of data structures that are relevant to database systems. A large amount of effort has been expended in the development of suitable data models. Several of these models distinguish between classes of entities and relationships among classes of entities in the abstractions they use for modelling the data. Among these models are the network model, Schmid and Swenson's model, the entity-relationship model, Navathe and Schkolnick's model, the semantic data model, and the structural model.

Properties of relationships and their representation

One of the main reasons that current commercial DBMSs provide only minimal support for temporal concepts is the size and complexity involved. Object-oriented databases, on the other hand, were developed to deal with complex database applications. Because temporal concepts require complex type support and advanced modeling concepts, object-oriented databases are excellent candidates for realization of temporal databases without requiring fundamental extensions to the basic data model. The ODMG (Object Data Management Group) has proposed a standard for object-oriented databases, including a standard object model, an object query language (OQL), and an object definition language (ODL). These do not include temporal support except at the data type level, as in SQL2. We present a language extension to OQL to accommodate time information. Our goal is not to propose a new temporal query language, but to incorporate temporal features into the existing OQL framework.

A temporal object query language

XML was initially developed for document management, but it is becoming increasingly used for storing and exchanging all kinds of data on the Internet. In this paper, we introduce a design methodology for XML schemas that is based upon well-understood conceptual modeling methodologies. Because XML is hierarchical (tree-structured), many different XML schemas (or document structures) can be generated from the same conceptual database schema. We describe algorithms for generating customized hierarchical views from EER model, creating XML schemas from hierarchical views, and creating XML instance documents.

Conceptual Modeling for Customized XML Schemas

This report presents a model to be used for database design. Because our motivation extends to providing guidance for the structured implementation of a database, we call our model the ''Structural Model.'' We derive the design using criteria of correctness, relevance, and performance from semantic and operational specifications obtained from multiple sources. These sources typically correspond to prospective users or user groups of the database. The integration of such specifications is a central issue in the development of an integrated structural database model. The structural model is used for the design of the logical structures that represent a real-world situation. However, it is not meant to represent all possible real-world semantics, but a subset of the semantics which are important in database modelling. The model uses relations as building blocks, and hence can be considered as an extension of Codd''s relational model [1970]. The main extensions to the relational model are the explicit representation of logical connections between relations, the inclusion of insertion-deletion constraints in the model itself, and the separation of relations into several structural types. Connections between relations are used to represent existence dependencies of tuples in different relations. These existence dependencies are important for the definition of semantics of relationships between classes of real-world entities. The connections between relations are used to specify these existence dependencies, and to ensure that they remain valid when the database is updated. Hence, connections implicitly define a basic, limited set of integrity constraints on the database, those that identify and maintain existence dependencies among tuples from different relations. Consequently, the rules for the maintenance of the structural integrity of the model under insertion and deletion of tuples are easy to specify. Structural relation types are used to specify how each relation may be connected to other relations in the model. Relations are classified into five types: primary relations, referenced relations, nest relations, association relations, and lexicon relations. The motivation behind the choice of these relation types is discussed, as is their use in data model design. A methodology for combining multiple, overlapping data models - also called user views in the literature - is associated with the structural model. The database model, or conceptual schema, which represents the integrated database, may thus be derived from the individual data models of the users. We believe that the structural model can be used to represent the data relationships within the conceptual schema of the ANSI/SPARC DBMS model since it can support database submodels, also called external schema, and maintain the integrity of the submodels with respect to the integrity constraints expressable in the structural model. We then briefly discuss the use of the structural model in database

A structural model for database systems

Most work on database integration has considered only support for data retrieval, not support for updates, and often the use of a special semantically rich data model has been required. In this paper we present an approach to database integration which supports updates and which uses only the standard relational data model. Many of the ideas used in this approach are applicable to database integration in the context of other data models as well.

Ramez Elmasri

Papers

Properties of relationships and their representation

A temporal object query language

Conceptual Modeling for Customized XML Schemas

A structural model for database systems

Integrating relational databases with support for updates