SLF4SS: Facilitating Flexible Services Selection

Author

Wang, Hongbing ; Wang, Yifei ; Huang, Joshua Zhexue ; Xu, Xun

Author_Institution

Dept. of Comput. Sci. & Eng., Southeast Univ., Nanjing

fYear

2006

fDate

Dec. 2006

Firstpage

312

Lastpage

315

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 users 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

Keywords

Web services; fuzzy logic; software selection; unsupervised learning; SLF4SS; SOA; Web services; fuzzy logic; machine learning; self-learning framework; services selection; Availability; Computer science; Constraint optimization; Costs; Delay; Fuzzy logic; Intelligent agent; Learning systems; Service oriented architecture; Web services;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Intelligence and Intelligent Agent Technology Workshops, 2006. WI-IAT 2006 Workshops. 2006 IEEE/WIC/ACM International Conference on

Conference_Location

Hong Kong

Print_ISBN

0-7695-2749-3

Type

conf

DOI

10.1109/WI-IATW.2006.122

Filename

4053259