Premium
Learning improves service discovery
Author(s) -
Olson Andrew M.,
Raje Rajeev R.,
Devaraju Barun,
Gallege Lahiru S.
Publication year - 2015
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3323
Subject(s) - service discovery , computer science , service (business) , matching (statistics) , quality of service , resource (disambiguation) , web service , publication , data science , data mining , world wide web , computer network , mathematics , advertising , statistics , business , economy , economics
Summary A distributed system of services assembled according to a service‐oriented architecture requires an efficient mechanism to discover appropriate services deployed over a network. The recent emergence of many service marketplaces makes the case for the existence of such a discovery service. These marketplaces typically provide rudimentary techniques to publish service information and associated matching activities. Such simple matching techniques are typically not suitable to address complex user requirements. Therefore, it is a challenge to discover relevant services, with a high degree of accuracy, out of existing choices. This paper discusses experiments performed on a discovery service whose search techniques incorporate learning profiles to accomplish these complex tasks. The UniFrame Resource Discovery System, which searches for required services, provided an experimental test bed for these experiments. The article describes these techniques and explains their algorithms. Experimental results illustrate the gains in the quality of selected services and reduction in the discovery time using the proposed techniques. Copyright © 2014 John Wiley & Sons, Ltd.