Randomised optimisation of discrimination networks considering node-sharing

Because of their ability to store, access, and process large amounts of data, Database Management Systems (DBMSs) and Rule-based Systems (RBSs) are used in many information systems as information processing units. A basic function of a RBS and a function of many DBMSs is to match conditions on the available data. To improve performance intermediate results are stored in Discrimination Networks (DNs). The resulting memory consumption and runtime cost depend on the structure of the DN. A lot of research has been done in the area of optimising DNs. In this paper, we focus on re-using network parts considering multiple rule conditions and exploiting the characteristics of equivalences. We present an approach incorporating the potential of both concepts and balance their application in a randomised fashion. To evaluate the algorithms developed, they were implemented and yielded promising results. Shortcomings of this approach are discussed and their removal constitutes our current work.