Advanced data models and knowledge models together with their powerful query and manipulation languages have already proven to be essential for systems that support non-standard applications such as engineering and knowledge-based application systems. In order to raise their usability and acceptability, it is overly important to provide adequate implementation techniques that guarantee extensible and efficient processing for this advanced DBMS scenario. In this paper we present design alternatives and implementation techniques for such kinds of advanced DBMS, strongly focussing on query and knowledge processing in client/server architectures. To discuss our considerations and implementation technologies, we refer to the knowledge-processing framework of the KBMS KRISYS, although our ideas are generally applicable to (advanced) DBMS.

A great variety of techniques has been developed to optimize and enhance query processing for relational, client/server, distribute~ parallel, and heterogeneous database systems (DBS).Baaed on that work and experience, we investigate how far those techniques are applicable to query processing in Knowledge Base Management Systems (KBMS).Our reference system is the KRISYS KBMS that consists of a knowledge-processing system at the client (clientbaaed processing) and a data-processing system at the server (database backend).We describe a unifying thrnework for query ,processing incorporating both processing systems (as realized in KRISYS).This allows to distribute and balance the amount of work done in the client and in the server.Based on an evaluation of that framework, severaf approaches to further enhance knowledge processing are reported.