Abstract
The process of web service mining intends to discover required services so as to provide the users with the services that are important and desired. While as the system that has been proposed has an important role in the recommendation of services to the users. Multiple techniques have been projected to execute the proposed actions, the collaborative filtering technique is mostly used for the recommended system here, we will describe different approaches which make use of collaborative filtering and also QOS, (a technical notation that is applied to the Web service mining). We will also discuss some methodologies of recommended system which use the multidimensional approach.
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References
Sharma, V., Kumar, M.: Web service discovery research: a study of existing approaches. Proc. Int. J. Recent Trends in Eng. Technol. 5(1) (2011)
Understanding quality of service for Web services. http://www.ibm.com/developerworks/library/ws-quality/index.html
Summary of Quality Model for Web Services. https://www.oasisopen.org/committees/download.php/15444/Comparsion.WSQMFE.doc
Felhi, F., Akaichi, J.: Real time self adaptable web services to the context: case study and performance evaluation. Int. J. Web Appl. (IJWA) 7(1), 1–9 (2015)
Grimberghe, A.K., Nanopoulos, A., Thieme, L.S.: A novel multidimensional framework for evaluating recommender systems. In: Proceedings of the ACM RecSys 2010 Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces (UCERSTI)
Rahman, M.M.: Contextual recommender systems using a multidimensional approach. Int. J. Emerg. Technol. Adv. Eng. (2013)
Uzun, A., Rack, C.: Using a Semantic Multidimensional Approach to Create a Contextual Recommender System
Adomavicius, G., Tuzhilin, A.: Extending Recommender Systems: A Multidimensional Approach
Thor, A., Rahm, E.: AWESOME A Data Warehouse-based System for Adaptive Website Recommendations
Pozveh, M.H., Nematbakhsh, M., Movahhedinia, N.: A multidimensional approach for context-aware recommendation in mobile commerce. (IJCSIS) Int. J. Comput. Sci. Inf. Secur. 3(1) (2009)
Adomavicius, G., Sankaranarayanan, R.: Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach
Giacometti, A., Marcel, P., Negre, E.: A Framework for Recommending OLAP Queries
Giacometti, A., Marcel, P., Negre, E.: Recommending Multidimensional Queries
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence, vol. 2009 (2009)
Urmela, S., Joseph, K.S.: An effective web service selection based on hybrid collaborative filtering and QoS-Trust evaluation. Int. J. Adv. Res. Comput. Eng. Technol. (2015)
Lina, S.Y., Lai, C.H., Wu, C.H., Lo, C.C.: A trustworthy QoS-based collaborative filteting approach for Webservice discovery. J. Syst. Soft. 93 (2014)
Patil, N.K., Pawar, P., More, S., Tupe, B.: Web Service recommendation using qos parameters and users location. Int. J. Adv. Res. Comput. Commun. Eng. 4(3) (2015)
Kokash, N.: Web Service Discovery with Implicit QoS Filtering
Gurjar, N.R., Rode, S.V.: Personalized QoS-aware Web service recommendation via exploiting location and collaborative filtering. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(1) (2015)
Yu, Q.: QoS-aware service selection via collaborative QoS evaluation
Chen, M., Ma, Y.: A Hybrid Approach to Web Service Recommendation Based on QoS-Aware Rating and Ranking
Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H.: Personalized QoS-Aware Web Service Recommendation and Visualization
Suria, S., Palanivel, K.: An enhanced web service recommendation system with ranking QoS information. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS)
Zhang, L., Zhang, B., Pahl, C., Xu, L., Zhu, Z.: Personalized Quality Prediction for Dynamic Service Management based on Invocation Patterns
Coello, J.M.A., Tobar, C.M., Yuming, Y.: Improving the performance of Web service recommenders using semantic similarity. JCS&T 14(2), 80 (2014)
Mohalkar, R., Fadtare, K., Todkar, S.: Web service recommendation via quality of service information. Int. J. Comput. Sci. Inf. Technology Research 3(2), 517−524 (2015)
Makhlughian, M., Hashemi, S., Rastegari, Y., Pejman, E.: Web service selection based on ranking of qos using associative classification. Int. J. Web Serv. Comput. (IJWSC), 3(1) (2012)
Sachan, D., Dixit, S., Kumar, S.: QoS aware formalized model for semantic Web service selection. Int. J. Web Semant. Technol. (IJWesT) 5(4) (2014)
Ran, S.: A Model for Web Services Discovery with QoS
Yu, T., Lin, K.: Service selection algorithms for Web services with end-to-end QoS constraints. IseB 3, 103–126 (2005). doi:10.1007/s10257-005-0052-z.19
Khutade, P., Phalnikar, R.: QOS based Web service discovery using oo concepts. Int. J. Adv. Technol. Eng. Res. (IJATER)
D’Mello, D.A., Ananthanarayana, V.S.: Semantic Web service selection based on service provider’s business offerings. In: IJSSST, vol. 10, no. 2
Alrifai, M., Risse, T., Dolog, P., Nejdl, W.: A scalable approach for QoS-based Web service selection
Kritikos, K., Plexousakis, D.: Requirements for QoS-based Web service description and discovery
Shi, Y., Zhang, J.K., Liu, B., Cui, L.: A new QoS prediction approach based on user clustering and regression algorithms. In: IEEE International Conference on Web Services (ICWS), Washington, DC, 4−9 July 2011. ISBN: 978-0-7695-4463-2
Negre, E.: Exploration collaborative de cubes de données. Doctoral thesis, University François Rabelais Tours (2009)
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Feddaoui, I., Felhi, F., Bergi, I.H., Akaichi, J. (2016). A Survey on Web Service Mining Using QoS and Recommendation Based on Multidimensional Approach. In: Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2016. Smart Innovation, Systems and Technologies, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-39345-2_38
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DOI: https://doi.org/10.1007/978-3-319-39345-2_38
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