Multi-data models translations in interoperable information systems

Abstract

Interoperation of heterogeneous and autonomous information systems has traditionally been hampered by semantic differences in their data models. In this paper, we address the problem by defining a methodology called TIME, which is based on an extensible meta model. Its key features are: a set of meta-types which can be used to represent the syntax and the semantics of data modeling concepts, a knowledge base of transformation rules that map a meta-type into other meta-types, and an inference engine which uses the transformation rules to translate schema from source to target models. The extensibility of the meta-model is achieved by organizing the meta-types into a generalization hierarchy that record similarities among modeling concepts. The hierarchy of meta-types allows the reuse of transformation rules during automatic generation of data model translators.