Combining skyline and multi-criteria decision methods to enhance Web services selection

This paper describes a new approach based on combining three Multi-Criteria Decision Making methods: Skyline, AHP and Promethee, for ranking Web services. The Skyline is used to reduce the search space and focusing only on interesting web services that are not dominated by any other service. AHP is used to weight QoS criteria in a simple and an intuitive manner; it allows for checking whether the weights assigned are consistent. Promethee method is leveraged to rank skyline services, by taking advantage of the outranking relationships between skyline candidate services and generating positive, negative and Net flows. An algorithm is proposed to rank skyline services based on Net flow. A case study based on an example of QoS dataset is presented to illustrate the different steps of our approach. The experimental evaluation conducted on real Dataset demonstrates that our approach can better capture the user preferences and retrieve the best ranked Skyline services.

[1]  Athman Bouguettaya,et al.  Computing Service Skyline from Uncertain QoWS , 2010, IEEE Transactions on Services Computing.

[2]  R. W. Saaty,et al.  The analytic hierarchy process—what it is and how it is used , 1987 .

[3]  Donald Kossmann,et al.  The Skyline operator , 2001, Proceedings 17th International Conference on Data Engineering.

[4]  Jean Pierre Brans,et al.  HOW TO SELECT AND HOW TO RANK PROJECTS: THE PROMETHEE METHOD , 1986 .

[5]  Eyhab Al-Masri,et al.  QoS-based Discovery and Ranking of Web Services , 2007, 2007 16th International Conference on Computer Communications and Networks.

[6]  Shrikant Mulik,et al.  The Analytical Hierarchy Process Approach for Prioritizing Features in the Selection of Web Service , 2008, 2008 Sixth European Conference on Web Services.

[7]  Djamal Benslimane,et al.  Selecting Skyline Web Services for Multiple Users Preferences , 2012, 2012 IEEE 19th International Conference on Web Services.

[8]  Chi-Hung Chi,et al.  An Enhanced PROMETHEE Model for QoS-Based Web Service Selection , 2011, 2011 IEEE International Conference on Services Computing.

[9]  Sanjiva Weerawarana,et al.  Unraveling the Web services web: an introduction to SOAP, WSDL, and UDDI , 2002, IEEE Internet Computing.

[10]  Bernhard Seeger,et al.  An optimal and progressive algorithm for skyline queries , 2003, SIGMOD '03.

[11]  Athman Bouguettaya,et al.  Foundations for Efficient Web Service Selection , 2009 .

[12]  Beng Chin Ooi,et al.  Efficient Progressive Skyline Computation , 2001, VLDB.

[13]  Junfeng Zhao,et al.  Personalized QoS Prediction forWeb Services via Collaborative Filtering , 2007, IEEE International Conference on Web Services (ICWS 2007).

[14]  Karim Benouaret Advanced techniques for Web service query optimization. (Techniques avancées pour l'optimisation de requêtes de services Web) , 2012 .

[15]  Johan Springael,et al.  PROMETHEE and AHP: The design of operational synergies in multicriteria analysis.: Strengthening PROMETHEE with ideas of AHP , 2004, Eur. J. Oper. Res..

[16]  Eyhab Al-Masri,et al.  Crawling multiple UDDI business registries , 2007, WWW '07.

[17]  Thomas Risse,et al.  Selecting skyline services for QoS-based web service composition , 2010, WWW '10.