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清华大学工业深度减碳论坛
报告题目:
Grid Service Discovery with Rough Sets
 报告人:
Maozhen Li
School of Engineering and Design, Brunel University, West London, UK
报告时间:
2008-01-04 11:00
报告地点:
4-312, FIT Building
主办单位:
Research Institute of Information Technology (信研院)
  简介:
 

2008年清华大学信息技术研究院系列学术报告1

 

Dr. Maozhen Li received the PhD in 1997 from Institute of Software, Chinese Academy of Sciences, Beijing. He is a Lecturer in the School of Engineering and Design at Brunel University. His research interests are in the areas of grid computing, distributed problem-solving environments for large-scale simulations, intelligent systems, service-oriented computing, semantic web. He has over 50 publications in these areas. He authored “The Grid: Core Technologies”, a research-level textbook on grid computing published by Wiley in 2005. This book was translated into Chinese and published in China by Tsinghua University Publisher in 2006. He has been serving as a TPC member for various conferences in the area of grid computing, e.g. IEEE CCGrid’05, CCGrid’06, CCGrid’07, CCGrid’08, IEEE SKG’05, SKG’06, SKG’07, IEEE CSE’08. He is on the editorial boards of Encyclopedia of Grid Computing Technologies and Applications and the International Journal of Grid and High Performance Computing. He is a member of IEEE.

Abstract

The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilizing grid facilities. This paper presents ROSSE, a Rough sets based search engine for grid service discovery. Building on Rough sets theory, ROSSE is novel in its capability to deal with uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct non-functional properties related to Quality of Service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximize user satisfaction in service discovery.

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