Sources of health information are widely distributed and involve many different processes and interactions. They may contain individual patient information collected and stored by a person or a combination of many sets of individual information collected by more than one entity. Moreover, patient data is not stored in only one central instance; it can be stored in databases which differ in size and structure, and it may also travel from sensors to mobile devices to servers. The process to access patient data requires that all the services and objects are connected to make data from the different healthcare providers available. In this paper we propose the design of a distributed multi agent architecture to monitor patients with heart disease. By empowering the data sources it is possible to manage and process patient data in order to react in time to individual health risk scenarios.

This paper describes the rationale and an incremental approach to introduce a new curriculum in the area of service management and engineering. This is done in the context of an existing software engineering and information system program. So the total cost of the new curriculum is not high. The approach, with relatively low start-up costs, is suitable for small departments. As with any new programme or degree there is a problem of getting the concepts known in the marketplace. The School of IT in initiating this programme is actively working with several organizations to promote the ideas to potential employees and students.

Experience-based reasoning (EBR) is a reasoning paradigm that has been used in almost every human activity such as business, military missions, and teaching activities since early human history. However, EBR has not been seriously studied from either a logical or mathematical viewpoint, although case-based reasoning (CBR) researchers have paid attention to EBR to some extent. This article will attempt to fill this gap by providing a unified fuzzy logic-based treatment of EBR. More specifically, this article first reviews the logical approach to EBR, in which eight different rules of inference for EBR are discussed. Then the article proposes fuzzy logic-based models to these eight different rules of inference that constitute the fundamentals for all EBR paradigms from a fuzzy logic viewpoint, and therefore will form a theoretical foundation for EBR. The proposed approach will facilitate research and development of EBR, fuzzy systems, intelligent systems, knowledge management, and experience management. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 867–889, 2007.

This article examines new issues resulting from applying case-based reasoning (CBR) in e-commerce and proposes a unified logical model for CBR-based e-commerce systems (CECS) that consists of three cycles and covers almost all activities of applying CBR in e-commerce. This article also decomposes case adaptation into problem adaptation and solution adaptation, which not only improves the understanding of case adaptation in the traditional CBR, but also facilitates the refinement of activity of CBR in e-commerce and intelligent support for e-commerce. It then investigates CBR-based product negotiation. This article thus gives insight into how to use CBR in e-commerce and how to improve the understanding of CBR with its applications in e-commerce from a logical viewpoint. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 29–46, 2005.

This article introduces abductive case-based reasoning (CBR) and attempts to show that abductive CBR and deductive CBR can be integrated in clinical process and problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction, and deductive CBR. This article also investigates abductive case retrieval and deductive case retrieval using similarity relations, fuzzy similarity relations, and similarity metrics. The proposed approach demonstrates that the integration of deductive CBR and abductive CBR is of practical significance in problem solving such as system diagnosis and analysis, and will facilitate research of abductive CBR and deductive CBR. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 957–983, 2005.

Fraud, deception and their recognition have received increasing attention in multiagent systems (MAS), e-commerce, and agent societies. However, little attention has been given to the theoretical foundation for fraud and deception from a logical viewpoint. We fill this gap by arguing that experience-based reasoning (EBR) is a logical foundation for recognizing fraud and deception. It provides a logical analysis of deception, which classifies recognition of deception into knowledge-based deception recognition, inference-based deception recognition, and hybrid deception recognition. It will examine the relationship between EBR and fraud as well as deception. It uses EBR to recognize fraud and deception in e-commerce and MAS. The proposed approach will facilitate research and development of recognition of fraud and deception in e-commerce.

Case-based reasoning (CBR) has drawn considerable attention in artificial intelligence (AI) fields with many successful applications in systems such as e-commerce and multiagent systems. For the moment, research and development of CBR basically follows the traditional process model of CBR, i.e., the R4 model and problem space model introduced in 1994 and 1996, respectively. However, there has been no logical analysis for this popular CBR model. This article will fill this gap by providing a unified logical foundation for the CBR cycle. The proposed approach is based on an integration of traditional mathematical logic, fuzzy logic, and similarity-based reasoning. At the same time, we examine the CBR cycle from the knowledge-based (KB) viewpoint. The proposed logical approach can facilitate research and development of CBR. © 2003 Wiley Periodicals, Inc.

Similarity is a core concept in case-based reasoning (CBR), because case base building, case retrieval, and even case adaptation all use similarity or similarity-based reasoning. However, there is some confusion using similarity, similarity measures, and similarity metrics in CBR, in particular in domain-dependent CBR systems. This article attempts to resolve this confusion by providing a unified framework for similarity, similarity relations, similarity measures, and similarity metrics, and their relationship. This article also extends some of the well-known results in the theory of relations to similarity metrics. It appears that such extension may be of significance in case base building and case retrieval in CBR, as well as in various applied areas in which similarity plays an important role in system behavior. © 2002 Wiley Periodicals, Inc.

Abduction and deduction play a fundamental role in problem solving. The paper extends abduction and deduction to abductive CBR (case-based reasoning) and deductive CBR and shows that abductive CBR and deductive CBR can be integrated in problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction and deductive CBR. The paper also investigates abductive case retrieval and deductive case retrieval using similarity relations, fuzzy similarity relations and similarity metrics. The proposed approach demonstrates that the integration of abductive CBR and deductive CBR is of practical significance in problem solving such as system diagnosis and analysis.

Abstract Certain types of computer language popular in areas such as decision support systems have the property that language statements need not be written in execution sequence, as the correct evaluation order can be established by dependency analysis. The uncontrolled use of these ‘nonsequential’ languages can lead to the development of programs and systems that are difficult to comprehend and maintain. This paper describes a method of clustering such code statements into sequential (procedural) modules to clarify visual presentation. The technique is based on depth‐first search of directed graphs and will group those statements which are most strongly related to each other into a module.