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SSC Based RS: An Efficient Service Recommendation System for Handling Big Data Applications


  • Sathyabama University, Chennai -600119, Tamil Nadu, India
  • CSE Department, Jeppiaar Engineering College, Chennai -600119, Tamil Nadu, India


Objectives: To find an appropriate web service and reduce the time taken for introducing web service, to improve parallel processing, to reduce the complexity and to improve its scalability and efficiency in big data environment. Methods: MapReduce framework in Hadoop platform is for increase the efficiency and scalability in big data domain, SSC based RS, web service information are structured in hierarchical format. The proposed system calculates the semantic comparison between the big data applications. Findings: SSC based RS (Semantic Similarity Calculation Based Recommendation System) is used to efficiently suggest better services for the requested users, by using semantic dictionary the semantic similarity will be calculated. Here, the services are stored in the hierarchical structure will increase the recommendation process faster. An experimental result shows that our proposed algorithm provides a suitable recommended service compared to other existing approaches. Applications/Improvement: In Big Data the proposed technique improves the efficiency and scalability by applying MapReduce parallel processing standard on Hadoop environment.


Big Data, Hadoop, Map Reduce, Recommender System, Web Service.

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  • Kim KW, Park WJ, Park ST. A study on plan to improve illegal parking using big data. Indian Journal of Science and Technology. 2015 Sep; 8(21):1–5. DOI: 10.17485/ijst/2015/v8i21/78274.
  • Lynch C. Big data: how do your data grow?. Nature. 2008; 455(7209):28–9.
  • Chang F, Dean J, Ghemawat S, Hsieh WC. Bigtable: a distributed storage system for structured data. ACM Transactions on Computer Systems. 2008; 26(2):1–14.
  • Dou W, Zhang X, Liu J, Chen J. HireSome-II: towards privacy-aware cross-cloud service composition for big data applications. IEEE Transactions on Parallel and Distributed Systems. 26(2):455–466.
  • Linden G, Smith B, York J. Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing. 2003; 7(1):76–80.
  • Bjelica M. Towards T V recommender system experiments with user modelling. IEEE Transactions on Consumer Electronics. 2010; 56(3):1763–69.
  • Alduan M, Alvarez F, Menendez J, Baez O. Recommender system for sport videos based on user audiovisual consumption. IEEE Transactions on Multimedia. 2013; 14(6):1546–57.
  • Chen Y, Cheng A, Hsu W. Travel recommendation by mining people attributes and travel group types from community-contributed photos. IEEE Transactions on Multimedia. 2012; 25(6):1283–95.
  • Zheng Z, Wu X, Zhang Y, Lyu M, Wang J. QoS ranking pre-diction for cloud services. IEEE Transactions on Parallel and Distributed Systems. 2013; 24(6):1213–22.
  • Raj JR. Web service discovery based on computation of semantic similarity distance and QOS normalization. Indian Journal of Computer Science and Engineering. 2012 Apr–May; 3(2):566–68.
  • Tibermacine O, Tibermacine C, Cherif F. WSSim: a Tool for the Measurement of web service interface similarity. Proceedings of the French-speaking conference on Software Architectures (CAL'13), Toulouse: France; 2013 May.
  • Kokash N. A comparison of web service interface similarity measures. Proceedings of the 2006 conference on STAIRS 2006. Third Starting AI Researchers' Symposium, Amsterdam, The Netherlands; 2006. p. 220–31.
  • Liu F, Shi Y, Yu J, Wang T, Wu J. Measuring similarity of web services based on WSDL. Proceeding of: IEEE International Conference on Web Services, ICWS 2010, Miami, Florida, USA; 2010 Jul 5–10.
  • Kohrs A, Merialdo B. Clustering for collaborative filtering applications. Proceedings of CIMCA'99. IOS Press; 1999.
  • Ungar LH, Foster DP. A formal statistical approach to collaborative filtering. Proceedings of Conference on Automated Leading and Discovery (CONALD); 1998.
  • Symeonidis P, Nanopoulos A, Papadopoulos A, Manolopoulos Y. Nearest bi-clusters collaborative filtering. WEBKDD; 2006.
  • Zhao ZD, Shang MS. User-based collaborative-filtering recommendation algorithms on Hadoop. Third International Workshop on Knowledge Discovery and Data Mining; 2010. p. 478–81.
  • Liang H, Hogan J, Xu Y. Parallel user profiling based on folksonomy for large scaled recommender systems: an implementation of cascading mapreduce. Proceedings of the IEEE International Conference on Data Mining Workshops; 2010. p. 156–61.
  • Suchithra M, Ramakrishnan M. A survey on different web service discovery techniques. Indian Journal of Science and Technology. 2015 Jul; 8(15):1–5. Doi no:10.17485/ijst/2015/v8i15/70773.
  • Abramowicz W, Haniewicz K, Kaczmarek M, Zyskowski D. Architecture for web services filtering and clustering. Proceedings 2nd International Conference on Internet and Web Applications and Services; 2007.
  • Li M, Yang Y. Efficient clustering index for semantic Web service based on user preference. Proceedings International Conference on Computer Science and Information Processing; 2012. p. 291–94.
  • Zheng Q, Wang Y. The application of semi-supervised clustering in web services composition. Advances in Intelligent and Soft Computing. 2012; 169:683–88.
  • Chen L, Hu L, Zheng Z, Wu J, Yin J, Li Y, Deng S. WTCluster: Utilizing tags for web services clustering. Lecture Notes in Computer Science. 2011; 7084:204–18.
  • Skoutas D, Sacharidis D, Simitsis A, Sellis T. Ranking and clustering web services using multicriteria dominance relationships. IEEE Transactions on Services Computing. 2010; 3(3):163–77.


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