Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/77232
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Type: Conference paper
Title: WS-finder: a framework for similarity search of web services
Author: Ma, J.
Sheng, Q.
Liao, K.
Zhang, Y.
Ngu, A.
Citation: Proceedings of the 10th International Conference on Service-Oriented Computing, held in Shanghai, China, 12-15 November, 2012 / C. Liu, H. Ludwig, F. Toumani and Q. Yu (eds.): pp.313-327
Publisher: Springer-Verlag
Publisher Place: Germany
Issue Date: 2012
Series/Report no.: Lecture Notes in Computer Science; 7636
ISBN: 9783642343209
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Service-Oriented Computing (10th : 2012 : Shanghai, China)
Statement of
Responsibility: 
Jiangang Ma, Quan Z. Sheng, Kewen Liao, Yanchun Zhang and Annew H.H. Ngu
Abstract: Most existing Web service search engines employ keyword search over databases, which computes the distance between the query and the Web services over a fixed set of features. Such an approach often results in incompleteness of search results. The Earth Mover’s Distance (EMD) has been successfully used in multimedia databases due to its ability to capture the differences between two distributions. However, calculating EMD is computationally intensive. In this paper, we present a novel framework calledWS-Finder, which improves the existing keywordbased search techniques for Web services. In particular, we employ EMD for many-to-many partial matching between the contents of the query and the service attributes. We also develop a generalized minimization lower bound as a new EMD filter for partial matching. This new EMD filter is then combined to a k-NN algorithm for producing complete top-k search results. Furthermore, we theoretically and empirically show that WS-Finder is able to produce query answers effectively and efficiently.
Rights: © Springer-Verlag Berlin Heidelberg 2012
DOI: 10.1007/978-3-642-34321-6_21
Published version: http://dx.doi.org/10.1007/978-3-642-34321-6_21
Appears in Collections:Aurora harvest
Computer Science publications

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