The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1) What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development.
Pub. online:1 Jan 2012Type:Research ArticleOpen Access
Volume 23, Issue 3 (2012), pp. 369–390
Nowadays, ontologies play a central role in many computer science problems such as data modelling, data exchange, integration of heterogeneous data and models or software reuse. Yet, if many methods of ontology based conceptual data modelling have been proposed, only few attempts have been made to ontology axioms based modelling of business rules, which make an integral part of each conceptual data model. In this paper, we present the approach how ontology axioms can be used for business rules implementation. Our proposal we apply for the transformation of PAL (Protege Axiom Language) constraints (ontology axioms), which is based on KIF (Knowledge Interchange Format) and is part of KIF ontology, into OCL (Object Constraint Language) constraints, which are part of a UML class diagram. Z language is used to formalise the proposal and describe the transformation. The Axiom2OCL plug-in is created for automation of the transformation and a case study is carried out.