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Journal of Intelligence and Information Systems,
Vol. 13, No. 3, September 2007
Advanced Web Services Retrieval System using Matchmaking Algorithm
Okkyung Choi, JungWoo Lee , and Sangyong Han
Vol. 13, No. 3, Page: 1 ~ 15
Keywords : Semantic Web, Web Services, Ontology, Information Retrieval
Abstract
Recently, semantic web technology, represented by ontology building, is being combined with web services technology, creating 'Semantic Web Services' as a new promising field in information retrieval research. Accordingly, many brokering and matchmaking agents are being developed and used in the field. However, literature review revealed that most models do not take QoS(Quality of Services) into consideration. In this study, a QoS-augmented matchmaking algorithm is developed based on service availability, response time, maximum transaction amount, reliability, accessibility and price as critical QoS items. A prototype for Intelligent Semantic Web Services System is developed using publicly available data. Performance test was conducted and reported at the end.
Development of Forward chaining inference engine SMART-F using Rete Algorithm in the Semantic Web
Kyunbeom Jwong, Yong Uk Song , June Seok Hong, Wooju Kim, Myung Jin Lee , and Ji Hyoung Park
Vol. 13, No. 3, Page: 17 ~ 29
Keywords : Forward Chaining Inference Engine, OWL, RDF, SWRL, XML
Abstract
Inference engine that performs the brain of software agent in next generation's web with various standards based on standard language of the web, XML has to understand SWRL (Semantic Web Rule Language) that is a language to express the rule in the Semantic Web. In this research, we want to develop a forward inference engine, SMART-F (SeMantic web Agent Reasoning Tools-Forward chaining inference engine) that uses SWRL as a rule express method, and OWL as a fact express method. In the traditional inference field, the Rete algorithm that improves effectiveness of forward rule inference by converting if-then rules to network structure is often used for forward inference. To apply this to the Semantic Web, we analyze the required functions for the SWRL-based forward inference, and design the forward inference algorithm that reflects required functions of next generation's Semantic Web deducted by Rete algorithm. And then, to secure each platform's independence and portability in the ubiquitous environment and overcome the gap of performance, we developed management tool of fact and rule base and forward inference engine. This is compatible with fact and rule base of SMART-B that was developed. So, this maximizes a practical use of knowledge in the next generation's Web environment.
New Optimization Algorithm for Data Clustering
Jumi Kim
Vol. 13, No. 3, Page: 31 ~ 45
Keywords : Data Clustering, Optimization-Based Clustering, NP Method, Random Sampling
Abstract
Large data handling is one of critical issues that the data mining community faces. This is particularly true for computationally intense tasks such as data clustering. Random sampling of instances is one possible means of achieving large data handling, but a pervasive problem with this approach is how to deal with the noise in the evaluation of the learning algorithm. This paper develops a new optimization based clustering approach using an algorithm specifically designed for noisy performance. Numerical results show this algorithm better than the other algorithms such as PAM and CLARA. Also with this algorithm substantial benefits can be achieved in terms of computational time without sacrificing solution quality using partial data.
A Multi-agent System based on Genetic Algorithm for Integration Planning in a Supply Chain Management
Byung Joo Park , Hyung Rim Choi, and Moo Hong Kang
Vol. 13, No. 3, Page: 47 ~ 61
Keywords : Supply Chain Management, Production Planning, Distribution Planning, Genetic Algorithm
Abstract
In SCM (supply chain management), companies are pursuing a new approach through which overall functions within the supply chain, ranging from material purchase to production, distribution, and sales are designed, planned, and managed in an integrated way. The core functions among them are production planning and distribution planning. As these problems are mutually related, they should be dealt with simultaneously in an integrated manner. SCM is large-scale and multi-stage problems. Also, its various kinds of internal or external factors can, at any time, dynamically bring a change to the existing plan or situation. Recently, many enterprises are moving toward an open architecture for integrating their activities with their suppliers, customers and other partners within the supply chain. Agent-based technology provides an effective approach in such environments. Multi-agent systems have been proven suitable to represent domains such as supply chain networks which involve interactions among manufacturing organization, their customers, suppliers, etc. with different individual goals and propriety information. In this paper, we propose a multi-agent system based on the genetic algorithm that make it possible to integrate the production and distribution planning on a real-time basis in SCM. The proposed genetic algorithm produced near optimal solution and we checked that there is a great difference in the results between integrated planning and non-integrated planning.
Adaptive Hybrid Genetic Algorithm Approach to Multistage-based Scheduling Problem in FMS Environment
YoungSu Yun, and Kwanwoo Kim
Vol. 13, No. 3, Page: 63 ~ 82
Keywords : Adaptive hybrid Genetic Algorithm, Local search, Adaptive scheme, Flexible manufacturing systems
Abstract
In this paper, we propose an adaptive hybrid genetic algorithm (ahGA) approach for effectively solving multistage-based scheduling problems in flexible manufacturing system (FMS) environment. The proposed ahGA uses a neighborhood search technique for local search and an adaptive scheme for regulation of GA parameters in order to improve the solution of FMS scheduling problem and to enhance the performance of genetic search process, respectively. In numerical experiment, we present two types of multistage-based scheduling problems to compare the performances of the proposed ahGA with conventional competing algorithms. Experimental results show that the proposed ahGA outperforms the conventional algorithms.
An Integrated Data Mining Model for Customer Relationship Management
Im-Young Song, Tae-Seok Yi, Ki-Jeong Shin, and Kyung-Chang Kim
Vol. 13, No. 3, Page: 83 ~ 99
Keywords : Data mining, CRM, e-CRM, Clustering, Association rules, Personalize
Abstract
Nowadays, the advancement of digital information technology resulting in the increased interest of the management and the use of information has given stimulus to the research on the use and management of information. In this paper, we propose an integrated data mining model that can provide the necessary information and interface to users of scientific information portal service according to their respective classification groups. The integrated model classifies users from log files automatically collected by the web server based on users' behavioral patterns. By classifying the existing users of the web site, which provides information service, and analyzing their patterns, we proposed a web site utilization methodology that provides dynamic interface and user oriented site operating policy. In addition, we believe that our research can provide continuous web site user support, as well as provide information service according to user classification groups.
A Genetic Algorithm for Guideway Network Design of Personal Rapid Transit
Jin-Myung Won
Vol. 13, No. 3, Page: 101 ~ 117
Keywords : Personal Rapid Transit, Network Optimization Problem, Genetic Algorithm
Abstract
In this paper, we propose a customized genetic algorithm (GA) to find the minimum-cost guideway network (GN) of personal rapid transit (PRT) subject to connectivity, reliability, and traffic capacity constraints. PRT is a novel transportation concept, where a number of automated taxi-sized vehicles run on an elevated GN. One of the most important problems regarding PRT is how to design its GN topology for given station locations and the associated inter-station traffic demands. We model the GN as a directed graph, where its cost, connectivity, reliability, and node traffics are formulated. Based on this formulation, we develop the GA with special genetic operators well suited for the GN design problem. Such operators include steady state selection, repair algorithm, and directed mutation. We perform numerical experiments to determine the adequate GA parameters and compare its performance to other optimization algorithms previously reported. The experimental results verify the effectiveness and efficiency of the proposed approach for the GN design problem having up to 210 links.
A Multi-Agent framework for Distributed Collaborative Filtering
Ae-Ttie Ji, Cheol Yeon , Seung-Hun Lee, Heung-Nam Kim, and Geun-Sik Jo
Vol. 13, No. 3, Page: 119 ~ 140
Keywords : Multi-Agent System, Distributed Recommender System, Collaborative Filtering, Web of Trust, Social Network
Abstract
Recommender systems enable a user to decide which information is interesting and valuable in our world of information overload. As the recent studies of distributed computing environment have been progressing actively, recommender systems, most of which were centralized, have changed toward a peer-to-peer approach. Collaborative Filtering (CF), one of the most successful technologies in recommender systems, presents several limitations, namely sparsity, scalability, cold start, and the shilling problem, in spite of its popularity. The move from centralized systems to distributed approaches can partially improve the issues; distrust of recommendation and abuses of personal information. However, distributed systems can be vulnerable to attackers, who may inject biased profiles to force systems to adapt their objectives. In this paper, we consider both effective CF in P2P environment in order to improve overall performance of system and efficient solution of the problems related to abuses of personal data and attacks of malicious users. To deal with these issues, we propose a multi-agent framework for a distributed CF focusing on the trust relationships between individuals, i.e. web of trust. We employ an agent-based approach to improve the efficiency of distributed computing and propagate trust information among users with effect. The experimental evaluation shows that the proposed method brings significant improvement in terms of the distributed computing of similarity model building and the robustness of system against malicious attacks. Finally, we are planning to study trust propagation mechanisms by taking trust decay problem into consideration.
Applying Polite level Estimation and Case-Based Reasoning to Context-Aware Mobile Interface System
Ohbyung Kwon , Sukjae Choi, and Tea Hwan Park
Vol. 13, No. 3, Page: 141 ~ 160
Keywords : Politeness Level, HCI, Context-Aware Computing, Case-Based Reasoning, Minkowski Aggregation Method
Abstract
User interface has been regarded as a crucial issue to increase the acceptance of mobile services. In special, even though to what extent the machine as speaker communicates with human as listener in a timely and polite manner is important, fundamental studies to come up with these issues have been very rare. Hence, the purpose of this paper is to propose a methodology of estimating politeness level in a certain context-aware setting and then to design a context-aware system for polite mobile interface. We will focus on Korean language for the polite level estimation simply because the polite interface would highly depend on cultural and linguistic characteristics. Nested Minkowski aggregation model, which amends Minkowski aggregation model, is adopted as a privacy-preserving similarity evaluation for case retrieval under distributed computing environment such as ubiquitous computing environment. To show the feasibility of the methodology proposed in this paper, simulation-based experiment with drama cases has performed to show the performance of the methodology proposed in this paper.
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