SLF4SS: Facilitating Flexible Services Selection

Authors:
Hongbing Wang;Yifei Wang;Joshua Zhexue Huang;Xun Xu
Affiliations:
Southeast University, China;Southeast University, China;The University of Hongkong, Hong Kong;Southeast University, China
Venue:
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Year:
2006

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Abstract

In this paper, we present SLF4SS, a self-learning framework for services selection. The main features of SLF4SS include (1) learning from previous match samples to help users discover more appropriate services, (2) using multi-dimensional properties to represent services for evaluation and selection, (3) optimizing the overall property of composite service appropriate to customer's constraints and preferences, and (4) addressing user's uncertain, vague requests. SLF4SS can simplify selection of suitable Web services in building high level services for various business applications, reduce implementation cost, and shorten the time of deploying enterprises applications based on SOA.