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Ranking and Clustering Web Services Using Multicriteria Dominance Relationships
July-September 2010 (vol. 3 no. 3)
pp. 163-177
ASCII Text
x 
 
Dimitrios Skoutas, Dimitris Sacharidis, Alkis Simitsis, Timos Sellis, "Ranking and Clustering Web Services Using Multicriteria Dominance Relationships," IEEE Transactions on Services Computing, vol. 3, no. 3, pp. 163-177, July-September, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TSC.2010.14,
author = {Dimitrios Skoutas and Dimitris Sacharidis and Alkis Simitsis and Timos Sellis},
title = {Ranking and Clustering Web Services Using Multicriteria Dominance Relationships},
journal ={IEEE Transactions on Services Computing},
volume = {3},
number = {3},
issn = {1939-1374},
year = {2010},
pages = {163-177},
doi = {http://doi.ieeecomputersociety.org/10.1109/TSC.2010.14},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Services Computing
TI - Ranking and Clustering Web Services Using Multicriteria Dominance Relationships
IS - 3
SN - 1939-1374
SP163
EP177
EPD - 163-177
A1 - Dimitrios Skoutas,
A1 - Dimitris Sacharidis,
A1 - Alkis Simitsis,
A1 - Timos Sellis,
PY - 2010
KW - Web services matchmaking
KW - ranking
KW - clustering
KW - skyline.
VL - 3
JA - IEEE Transactions on Services Computing
ER -
 
Dimitrios Skoutas, L3S Research Center, Hannover
Dimitris Sacharidis, Institute for the Management of Information Systems, Athens
Alkis Simitsis, HP Labs, Palo Alto
Timos Sellis, Institute for the Management of Information Systems, Athens
As the web is increasingly used not only to find answers to specific information needs but also to carry out various tasks, enhancing the capabilities of current web search engines with effective and efficient techniques for web service retrieval and selection becomes an important issue. Existing service matchmakers typically determine the relevance between a web service advertisement and a service request by computing an overall score that aggregates individual matching scores among the various parameters in their descriptions. Two main drawbacks characterize such approaches. First, there is no single matching criterion that is optimal for determining the similarity between parameters. Instead, there are numerous approaches ranging from Information Retrieval similarity measures up to semantic logic-based inference rules. Second, the reduction of individual scores to an overall similarity leads to significant information loss. Determining appropriate weights for these intermediate scores requires knowledge of user preferences, which is often not possible or easy to acquire. Instead, using a typical aggregation function, such as the average or the minimum of the degrees of match across the service parameters, introduces undesired bias, which often reduces the accuracy of the retrieval process. Consequently, several services, e.g., those having a single unmatched parameter, may be excluded from the result set, while being potentially good candidates. In this work, we present two complementary approaches that overcome the aforementioned deficiencies. First, we propose a methodology for ranking the relevant services for a given request, introducing objective measures based on dominance relationships defined among the services. Second, we investigate methods for clustering the relevant services in a way that reveals and reflects the different trade-offs between the matched parameters. We demonstrate the effectiveness and the efficiency of our proposed techniques and algorithms through extensive experimental evaluation on both real requests and relevance sets, as well as on synthetic scenarios.

[1] X. Dong, A.Y. Halevy, J. Madhavan, E. Nemes, and J. Zhang, "Similarity Search for Web Services," Proc. 30th Int'l Conf. Very Large Data Bases (VLDB), pp. 372-383, 2004.
[2] M. Paolucci, T. Kawamura, T.R. Payne, and K.P. Sycara, "Semantic Matching of Web Services Capabilities," Proc. First Int'l Semantic Web Conf. (ISWC), pp. 333-347, 2002.
[3] M. Klusch, B. Fries, and K.P. Sycara, "Automated Semantic Web Service Discovery with OWLS-MX," Proc. Fifth Int'l Joint Conf. Autonomous Agents and Multiagent Systems (AAMAS), pp. 915-922, 2006.
[4] D. Skoutas, D. Sacharidis, A. Simitsis, V. Kantere, and T.K. Sellis, "Top-k Dominant Web Services under Multi-Criteria Matching," Proc. 12th Int'l Conf. Extending Database Technology: Advances in Database Technology (EDBT), pp. 898-909, 2009.
[5] J. Pei, B. Jiang, X. Lin, and Y. Yuan, "Probabilistic Skylines on Uncertain Data," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB), pp. 15-26, 2007.
[6] J. Colgrave, R. Akkiraju, and R. Goodwin, "External Matching in UDDI," Proc. IEEE Int'l Conf. Web Services (ICWS), p. 226, 2004.
[7] J. Cardoso, "Discovering Semantic Web Services with and without a Common Ontology Commitment," Proc. IEEE Services Computing Workshops (SCW), pp. 183-190, 2006.
[8] D. Skoutas, A. Simitsis, and T. Sellis, "A Ranking Mechanism for Semantic Web Service Discovery," Proc. IEEE Services Computing Workshops (SCW), pp. 41-48, 2007.
[9] U. Bellur and R. Kulkarni, "Improved Matchmaking Algorithm for Semantic Web Services Based on Bipartite Graph Matching," Proc. IEEE Int'l Conf. Web Services (ICWS), pp. 86-93, 2007.
[10] W.-T. Balke and M. Wagner, "Cooperative Discovery for User-Centered Web Service Provisioning," Proc. IEEE Int'l Conf. Web Services (ICWS), pp. 191-197, 2003.
[11] F. Kaufer and M. Klusch, "WSMO-MX: A Logic Programming Based Hybrid Service Matchmaker," Proc. European Conf. Web Services (ECOWS), pp. 161-170, 2006.
[12] J. Caverlee, L. Liu, and D. Rocco, "Discovering and Ranking Web Services with BASIL: A Personalized Approach with Biased Focus," Proc. Second Int'l Conf. Service-Oriented Computing (ICSOC), pp. 153-162, 2004.
[13] J. Ma, Y. Zhang, and J. He, "Efficiently Finding Web Services Using a Clustering Semantic Approach," Proc. Int'l Workshop Context Enabled Source and Service Selection, Integration and Adaptation (CSSSIA), p. 5, 2008.
[14] S. Börzsönyi, D. Kossmann, and K. Stocker, "The Skyline Operator," Proc. 17th IEEE Int'l Conf. Data Eng. (ICDE), pp. 421-430, 2001.
[15] J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, "Skyline with Presorting," Proc. 19th IEEE Int'l Conf. Data Eng. (ICDE), pp. 717-816, 2003.
[16] I. Bartolini, P. Ciaccia, and M. Patella, "Efficient Sort-Based Skyline Evaluation," ACM Trans. Database Systems, vol. 33, no. 4, pp. 1-45, 2008.
[17] D. Papadias, Y. Tao, G. Fu, and B. Seeger, "Progressive Skyline Computation in Database Systems," ACM Trans. Database Systems, vol. 30, no. 1, pp. 41-82, 2005.
[18] K.C.K. Lee, B. Zheng, H. Li, and W.-C. Lee, "Approaching the Skyline in Z Order," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB), pp. 279-290, 2007.
[19] M.L. Yiu and N. Mamoulis, "Efficient Processing of Top-k Dominating Queries on Multi-Dimensional Data," Proc. 33rd Int'l Conf. Very Large Data Bases (VLDB), pp. 483-494, 2007.
[20] C.Y. Chan, H.V. Jagadish, K.-L. Tan, A.K.H. Tung, and Z. Zhang, "Finding k-Dominant Skylines in High Dimensional Space," Proc. ACM SIGMOD, pp. 503-514, 2006.
[21] W.-T. Balke, U. Güntzer, and C. Lofi, "Eliciting Matters— Controlling Skyline Sizes by Incremental Integration of User Preferences," Proc. 12th Int'l Conf. Database Systems for Advanced Applications (DASFAA), pp. 551-562, 2007.
[22] X. Lin, Y. Yuan, Q. Zhang, and Y. Zhang, "Selecting Stars: The k Most Representative Skyline Operator," Proc. 23rd IEEE Int'l Conf. Data Eng. (ICDE), pp. 86-95, 2007.
[23] Y. Tao, L. Ding, X. Lin, and J. Pei, "Distance-Based Representative Skyline," Proc. 25th IEEE Int'l Conf. Data Eng. (ICDE), pp. 892-903, 2009.
[24] J.A. Aslam and M.H. Montague, "Models for Metasearch," Proc. ACM SIGIR, pp. 275-284, 2001.
[25] M.H. Montague and J.A. Aslam, "Condorcet Fusion for Improved Retrieval," Proc. Eleventh ACM Int'l Conf. Information and Knowledge Management (CIKM), pp. 538-548, 2002.
[26] M. Farah and D. Vanderpooten, "An Outranking Approach for Rank Aggregation in Information Retrieval," Proc. ACM SIGIR, pp. 591-598, 2007.
[27] E.A. Fox and J.A. Shaw, "Combination of Multiple Searches," Proc. Second Text REtrieval Conference (TREC), pp. 243-252, 1993.
[28] J.-H. Lee, "Analyses of Multiple Evidence Combination," Proc. ACM SIGIR, pp. 267-276, 1997.
[29] O. Zamir and O. Etzioni, "Grouper: A Dynamic Clustering Interface to Web Search Results," Computer Networks, vol. 31, nos. 11-16, pp. 1361-1374, 1999.
[30] S. Osinski, J. Stefanowski, and D. Weiss, "Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition," Proc. Int'l Conf. Intelligent Information Systems, pp. 359-368, 2004.
[31] S. Papadopoulos, D. Sacharidis, and K. Mouratidis, "Continuous Medoid Queries over Moving Objects," Proc. 10th Int'l Symp. Spatial and Temporal Databases (SSTD), pp. 38-56, 2007.
[32] R.A. Baeza-Yates and B.A. Ribeiro-Neto, Modern Information Retrieval. ACM Press/Addison-Wesley, 1999.
[33] M. Klusch and B. Fries, "Hybrid OWL-S Service Retrieval with OWLS-MX: Benefits and Pitfalls," Proc. First Int'l Joint Workshop Service Matchmaking and Resource Retrieval in the Semantic Web (SMRR), 2007.

Index Terms:
Web services matchmaking, ranking, clustering, skyline.
Citation:
Dimitrios Skoutas, Dimitris Sacharidis, Alkis Simitsis, Timos Sellis, "Ranking and Clustering Web Services Using Multicriteria Dominance Relationships," IEEE Transactions on Services Computing, vol. 3, no. 3, pp. 163-177, July-Sept. 2010, doi:10.1109/TSC.2010.14
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