Schema management for large-scale multidatabase systems

Advances in networking and database technologies have made the concept of global information sharing possible. A rapidly growing number of applications require access to and manipulation of the data residing in multiple pre-existing database systems, which are usually autonomous and heterogeneous. A promising approach to the problems of interoperating multiple heterogeneous database systems is the construction of multidatabase systems. Among all of the research issues concerning multidatabase systems, schema management which involves with the management of various schemas at different levels in a dynamic environment has been largely overlooked in the previous research. Two most important research in schema management have been identified: schema translation and schema integration. The need for declarative and extensible approach to schema translation and the support for schema integration are accentuated in a large-scale environment. This dissertation presents a construct-equivalence-based methodology based on the implications of semantics characteristics of data models for schema translation and schema integration. The research was undertaken for the purposes of (1) overcoming the methodological inadequacies of existing schema translation approaches and the conventional schema integration process for large-scale MDBSs, (2) providing an integrated methodology for schema translation and schema normalization whose similarities of problem formulation has not been previously recognized, (3) inductively learning model schemas that provide a basis for declaratively specifying construct equivalences for schema translation and schema normalization. The methodology is based on a metamodel (Synthesized Object-Oriented Entity-Relationship (SOOER) model), an inductive metamodeling approach (Abstraction Induction Technique), a declarative construct equivalence representation (Construct Equivalence Assertion Language, CEAL), and its associated transformation and reasoning methods. The results of evaluation studies showed that Abstraction Induction Technique inductively learned satisfactory model schemas. CEAL's expressiveness and adequacy in meeting its design principles, well-defined construct equivalence transformation and reasoning methods, as well as the advantages realized by the construct-equivalence-based schema translation and schema normalization suggested that the construct-equivalence-based methodology be a promising approach for large-scale MDBSs