A Real And Accurate Semantic Search Indexing Approach Using Asvm Machine In Big Data Analytics
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : Y.Krishna Bhargavi, Dr. Yelisetty Ssr Murthy, Dr.O.Srinivasa Rao
|DOI : 10.14445/22315381/IJETT-V69I2P221|
MLA Style: Y.Krishna Bhargavi, Dr. Yelisetty Ssr Murthy, Dr.O.Srinivasa Rao "A Real And Accurate Semantic Search Indexing Approach Using Asvm Machine In Big Data Analytics" International Journal of Engineering Trends and Technology 69.2(2021):144-159.
APA Style:Y.Krishna Bhargavi, Dr. Yelisetty Ssr Murthy, Dr.O.Srinivasa Rao. A Real And Accurate Semantic Search Indexing Approach Using Asvm Machine In Big Data Analytics. International Journal of Engineering Trends and Technology, 69(2), 144-159.
Big data is a receiver of information to give accurate search and relevant content for better work efficiency-experience. In earlier days, many semantic algorithms have been designed to improve effective content searching, but these are facing limitations. The information being retrieved and content filtering help future applications to get comfortable with operations. Web browsing and its recommendation systems are currently facing inaccurate content tracking, and hence the users cannot acquire the required information. In this research work, an adaptive SVM-based semantic search technique has been designed for big data applications. The method is calculating the performance measures like query time, building time, accuracy, average precision, stdError, SSR. Here, the presented KVASIR-ASVM architectural design encounters the existing systems and finally enhancing the accuracy to 99.72% and recalling at a rate of 0.997%. These experimental results outperform the methodology and compete with current technology.
 T. Berners-Lee, J. Hendler, O. Lassila, et al. The semantic web. Scientific American, 284(5)(2001) 28–37.
 M. Berry, S. Dumais, and G. O`Brien. Using linear algebra for intelligent information retrieval. SIAM Review, 37(4)(1995) 573–595.
 D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. J. Mach. Learn. Res., (2003) 3:993–1022.
 J. Bobadilla, F. Ortega, A. Hernando, and A. Gutirrez. Recommender systems survey. Knowledge-Based Systems, (2013).
 M. Brand. Fast low-rank modifications of the thin singular value decomposition. Linear Algebra and its Applications, (2006).
 J. S. Breese, D. Heckerman, and C. Kadie. The empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, (1998).
 L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and Zipf-like distributions: evidence and implications. In INFOCOM `99, IEEE, 1(1999) 126–134.
 R. Burke. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4)(2002) 331–370.
 M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn and S. Moon. I tube, you tube, everybody tubes: Analyzing the world`s largest user-generated content video system. In ACM IMC`07, (2007).
 M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn and S. Moon. Analyzing the video popularity characteristics of large-scale user-generated content systems. IEEE/ACM Trans. Netw., (2009).
 C. L. Clarke, M. Kolla, G. V. Cormack, O. Vechtomova, A. Ashkan, S. B¨ uttcher, and I. MacKinnon. Novelty and diversity in information retrieval evaluation. In ACM SIGIR`08, (2008).
 P. Cremonesi, A. Tripodi, and R. Turrin. Cross-domain recommender systems. In Data Mining Workshops (ICDMW), IEEE, (2011).
 S. Dasgupta and Y. Freund. Random projection trees and low dimensional manifolds. In ACM Theory of Computing, (2008).
 S. Dasgupta and K. Sinha. Randomized partition trees for exact nearest neighbor search. CoRR, abs/1302.1948 (2013).
 C. M. De Vries, L. De Vine, S. Geva, and R. Nayak. Parallel streaming signature em-tree: A clustering algorithm for web-scale applications. In International Conference on World Wide Web, (2015).
 S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for information science, 41(6) (1990) 391.
 W. B. Frakes and R. Baeza-Yates, editors. Information Retrieval: Data Structures and Algorithms. Prentice-Hall, Inc., USA, (1992).
 D. Glowacka, T. Ruotsalo, K. Konyushkova, K. Athukorala, S. Kaski, and G. Jacucci. Scent: A system for browsing scientific literature through keyword manipulation. In ACM International Conference on Intelligent User Interfaces Companion, (2013).
 D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM, 35(12)(1992) 61–70.
 K. Hajebi, Y. Abbasi-Yadkori, H. Shahbazi, and H. Zhang. Fast approximate nearest-neighbor search with the k-nearest neighbor graph. In International Joint Conference on Artificial Intelligence, IJCAI`11 1312–1317. AAAI Press, (2011).
 N. Halko, P. G. Martinsson, and J. A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM, Rev., (2011).
 M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. ACM SIGKDD Explor. Newsl., 11(1)(2009) 10–18.
 J. He, W. Liu, and S.-F. Chang. Scalable similarity search with optimized kernel hashing. In ACM SIGKDD, (2010).
 D. Huynh, S. Mazzocchi, and D. Karger. Piggybank: Experience the semantic web inside your web browser. In Y. Gil, E. Motta, V. Benjamins, and M. Musen, editors, The Semantic Web, (2005).
 V. Hyv¨onen, T. Pitk¨anen, S. Tasoulis, L. Wang, T. Roos, and J. Corander. Technical report: Fast k-nn search. arXiv preprint arXiv:1509.06957, (2015).
 Joseph George, Dr. M.K Jeyakumar A Comparative Analysis of Data Integration and Business Intelligence Tools with an Emphasis on Healthcare Data International Journal of Engineering Trends and Technology 68.9(2020):5-9.
 Y. Jia, J. Wang, G. Zeng, H. Zha, and X.-S. Hua. Optimizing KD-trees for scalable visual descriptor indexing. In IEEE Computer Vision and Pattern Recognition (CVPR), (2010) 3392–3399.
 Y. Koren and R. Bell. Advances in collaborative filtering. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, (2011) 145–186. Springer US,
 B. Li, Q. Yang, and X. Xue. Can movies and books collaborate?: Cross-domain collaborative filtering for sparsity reduction. In International Joint Conference on Artificial Intelligence, (2009).
 T. Liu, A. W. Moore, A. Gray, and K. Yang. An investigation of practical approximate nearest neighbor algorithms. In Advances in Neural Information Processing Systems, MIT Press, (2004).
 Wang, L., Tasoulis, S., Roos, T., &Kangasharju, J.., Kvasir: Scalable provision of semantically relevant web content on big data framework. IEEE Transactions on Big Data, 2(3)(2016) 219-233.
 H. Jegou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(1)(2011) 117–128.
semantic search, ASVM, bigdata, Internet, query time.