Advances in intelligent information processing

Abstract

Intelligent information processing has become increasingly popular over the last decade. Both large and medium companies are quickly learning that intelligent information processing is more and more a required element of doing business, and businesses around the world are investing substantial amounts of capital in acquiring and implementing intelligent information processing systems. As a result, there is a growing demand for research in intelligent information processing to meet the challenges related to the design, implementation and management of such systems. Because of the growing importance of the subject, there is a significant amount of ongoing research in the area. To allow both academic researchers and practitioners to come together, the International Conference on Intelligent Information Processing 2004 was held in Beijing, China, on 21–23 October 2004, sponsored by the International Federation for Information Processing Technical Committee 12 (IFIP TC12). Co-located with the main conference were three interesting workshops, including the Business Intelligence Workshop and the Intelligent Enterprise Computing Workshop (http://www.intsci.ac.cn/en/ifip/iip2004/workshop.jsp). Another related major event was the IFIP TC8 First International Conference on Research and Practical Issues on Enterprise Information Systems (http://www.confenis.org) held in Vienna, Austria, on 24–26 April 2006. These events have provided international forums for researchers in academia and industry to present their most recent findings in intelligent information processing, and a total of almost 120 papers were presented (Anjomshoaa et al., 2006; Goh, 2006; Gu et al., 2006; Guo et al., 2006; Li et al., 2006; Zhang & Zhang, 2006; Xu et al., 2007). The purpose of this special issue of Expert Systems is to report on the state of the art of, and emerging trends in, research and practice in intelligent information processing. This issue presents expanded versions of selected papers from both conferences plus regular submissions, following the call for papers for the issue. To prepare for this issue, all authors were asked to respond to two rounds of peer review. In the paper by Chaudhry, the problem of locating a maximum weighted number of facilities such that no two are within a specified distance from each other is addressed. An evolutionary approach, more specifically a genetic algorithm, is proposed to solve this problem. The meta-learning system for knowledge discovery in databases supports the multi-phase pipeline data mining process. The paper by Luo et al. presents a meta-learning system engine for knowledge discovery in databases in a multi-agent environment. Key issues such as process control and job assignment are resolved through coordinating various agents within a scheduling framework. The paper by Huq, Mann and Gosine presents a novel approach of intelligent sensory information processing for behaviour-based robot control using a fuzzy discrete event system. It employs a distributed fuzzy discrete event system to eliminate the formation of complex fuzzy predicates and a large fuzzy rule-base. Comprehensive experiments are also presented to authenticate the performance of the proposed method. Case-based reasoning has been applied to many industry sectors. However, the technique of case indexing and retrieving is still a challenging research issue. The paper by Ahn, Kim and Han presents a genetic algorithm for global optimization of nearest neighbours in case-based reasoning systems. The algorithm has been applied to a real-world business operations management case in Korea. A Petri net is a computational formalism widely used for modelling distributed and concurrent systems. The paper by Li et al. proposes a Petri-net-based Markov algorithm for analysing and managing the uncertainties in software project management. In group decision-making situations, individual opinions are usually characterized by subjectivity, imprecision and vag