Representation and Processing Algorithms for Business Rules Systems

Business Rules is a powerful technology for representing a business policy. It opens the chance to react fast on changes in the market. For example, new pricing strategies can be implemented without a time consuming re-programming of a sales software. The knowledge expressed by rules can be easily understood by non-computer experts. Business experts therefore are able to handle Business Rules directly and therefore make changes even faster. For this, the construction of a Global Semantic Graph (GSG) to support future information- and collaboration-centric applications and services, is a very important subject. The GSG is a publish/subscribe (pub/sub) based architecture that supports publication of t-uples and subscriptions with standing graph queries. An implementation of an efficient pattern matching algorithm such as Rete on top of a distributed environment might serve as a possible substrate for GSG’s pub/sub facility. Knowledge description and exploitation within a Business Rule Management System (BRMS) are somehow conflicting characteristics, since the increase of the representation power of knowledge diminishes the efficiency of the system and increases the difficulty of carrying it out. Many challenges in the BRMSs field are difficult to solve from a computational point of view.The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree) and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs).