Building quality into case-based reasoning systems

Abstract

Complex decision-support information systems for diverse domains need advanced facilities, such as knowledge repositories, reasoning systems, and modeling for processing interrelated information. System development must satisfy functional requirements, but must also systematically meet global quality factors, such as performance, confidentiality and accuracy, called non-functional requirements (NFRs).To build quality into an important class of decision support systems, case-based reasoning (CBR) systems, this paper presents “QualityCBR,” a goal-oriented, knowledge-based approach for systematically dealing with NFRs for CBR systems. With the idea that similar problems have similar solutions, CBR systems store cases (problems with solutions) and solve new problems by retrieving and reusing similar past cases. QualityCBR integrates existing work on CBR and NFRs. It helps developers state and refine NFRs, consider tradeoffs, make decisions and evaluate their impact on NFRs. We illustrate the approach in a complex medical domain, in vitro fertilization, where CBR suggests therapy for patients, predicts the probability for successful pregnancy, and determines patient's characteristics that can improve pregnancy rate.KeywordsUser FriendlinessImplementation AlternativeDevelopment GraphParent GoalFull ClusterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.