Cooperative Recognition of Interdatabase Dependencies

Abstract

A novel approach towards the recognition of interdatabase dependencies (IDD) using a federative agent system FCSI is presented. The architecture of the FCSI is designed as a set of coalition-based cooperative, intelligent agents each of them uniquely assigned to one autonomous local database system. The FCSI aims for a cooperative solution for the problem of searching for semantically related information while strictly respecting the autonomy requirements of each individual database system. For this purpose first a terminologically represented local domain information model on top of the local conceptual database schema is built at each agent by processing scripts specified by user’s intention on externally available semantic aspects or views of some local schema objects. These objects are then appropiately linked into the local information ontology by the local FCSI agent. Remote terminological classification then serves as a basis for the recognition of intentional IDDs between objects of different schemas with respect to some or all of their sofar intentionally related semantic aspects or views. Projections on respective associated aspect valuations at database state level then determine agent-directed, context-based data sharing and result in restricted proposals for interdatabase schema assertions. Methods for utilitarian coalition building among the rational agents of the FCSI are used in order to cooperatively search for semantically related data. The decentralized calculation of each agent’s utility bases on their local productions resulting from the execution of own and received tasks to find such dependencies between local terminological information models. There is no prior need and even no possibility to browse through non-local schema structures in order to find some possibly relevant data. In this paper the current status of ongoing research on the FCSI is reported.