A knowledge-based framework for intelligent-data migration

Abstract: The Object-Inferencing Framework (OIF) is a knowledge-based system developed for intelligent-data migration. The framework provides a mechanism to integrate relational data which represents a source model; a project-specific rulebase which specifies plausible migration scenarios; and a deduction system to facilitate the migration of source data to a new, complex target model. Typically, the target model includes constituents that possess both graphic and tabular components. Although the framework is experimental, industrial applications built upon OIF have been successfully deployed in scenarios in which the source data contained implicit information in that semantic relationships and structure conveyed by the data had to be inferred by a domain expert. This framework provides a substrate for migration from any unstructured or semi-structured data representation to a complex, semantically rich target data model. Examples of the migration of CAD data, which represents an electrical-distribution system, to a client-server based Automated Mapping/Facilities Management (AM/FM) platform are presented to convey the salient features of the design and utility of the OIF. Even though the examples are taken from a specific domain, the approach has potential applications in a myriad of domains, including business enterprises in which the migration of data created and managed by legacy systems to object-oriented and clientserver environments is an area of intense research and development.