Pattern Evaluation with Location Based Query Search

Authors(2) :-J. V. D Prasad, M. Sri Mounica

Customers are increasingly seeking complex task-oriented goals on the Web, such as making routes, managing finances or planning purchases. To this end, they usually break down the duties into a few co-dependent actions and problem multiple concerns around these actions repeatedly over quite a very lengthy time. To better support users in their long-term details missions on the Web, google keep track of their concerns and clicks while searching on the internet. In this document, we study the problem of organizing a user’s historical concerns into categories in an energetic and automated fashion. Instantly determining question categories is helpful for a number of different online look for engine components and applications, such as query suggestions, result position, question alterations, sessionization, and collaborative look for. So in this document we propose to develop Customized Location based Query Search method for pattern evaluation in accessing customer preference location leads to relevant details look for in relational database. This procedure automatically retrieve customer prefer locations centred on their longitude and permission of each customer in relational database.

Authors and Affiliations

J. V. D Prasad
Assistant Professor, Computer Science Department, VR Siddhartha College, Vijayawada, Andhra Pradesh, India
M. Sri Mounica
Student, Computer Science Department, VR Siddhartha College, Vijayawada, Andhra Pradesh, India

Pattern evaluation, Relational database, Query suggestion, Location based search

  1. R. Baeza-Yates, C. Hurtado, and M. Mendoza, "Query recommendation using query logs in search engines," in Proc. Int. Conf. Current Trends Database Technol., 2004, pp. 588–596.
  2. D. Beeferman and A. Berger, "Agglomerative clustering of a search engine query log," in Proc. 6th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2000, pp. 407–416.
  3. H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li, "Context-aware query suggestion by mining click-through and session data," in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2008, pp. 875–883.
  4. N. Craswell and M. Szummer, "Random walks on the click graph," in Proc. 30th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2007, pp. 239–246.
  5. Q. Mei, D. Zhou, and K. Church, "Query suggestion using hitting time," in Proc. 17th ACM Conf. Inf. Knowl. Manage., 2008, pp. 469–478.
  6. Y. Song and L.-W. He, "Optimal rare query suggestion with implicit user feedback," in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 901–910.
  7. T. Miyanishi and T. Sakai, "Time-aware structured query suggestion," in Proc. 36th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2013, pp. 809–812.
  8. A. Anagnostopoulos, L. Becchetti, C. Castillo, and A. Gionis, "An optimization framework for query recommendation," in Proc. ACM Int. Conf. Web Search Data Mining, 2010, pp. 161–170.
  9. P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna, "The query-flow graph: Model and applications," in Proc. 17th ACM Conf. Inf. Knowl. Manage., 2008, pp. 609–618.
  10. Y. Song, D. Zhou, and L.-w. He, "Query suggestion by constructing term-transition graphs," in Proc. 5th ACM Int. Conf. Web Search Data Mining, 2012, pp. 353–362.
  11. L. Li, G. Xu, Z. Yang, P. Dolog, Y. Zhang, and M. Kitsuregawa, "An efficient approach to suggesting topically related web queries using hidden topic model," World Wide Web, vol. 16, pp. 273–297, 2013.
  12. D. Wu, M. L. Yiu, and C. S. Jensen, "Moving spatial keyword queries: Formulation, methods, and analysis," ACM Trans. Database Syst., vol. 38, no. 1, pp. 7:1–7:47, 2013.
  13. D. Wu, G. Cong, and C. S. Jensen, "A framework for efficient spatial web object retrieval," VLDB J., vol. 21, no. 6, pp. 797–822, 2012.
  14. J. Fan, G. Li, L. Zhou, S. Chen, and J. Hu, "SEAL: Spatio-textual similarity search," Proc. VLDB Endowment, vol. 5, no. 9, pp. 824– 835, 2012.
  15. P. Bouros, S. Ge, and N. Mamoulis, "Spatio-textual similarity joins," Proc. VLDB Endowment, vol. 6, no. 1, pp. 1–12, 2012.
  16. Y. Lu, J. Lu, G. Cong, W. Wu, and C. Shahabi, "Efficient algorithms and cost models for reverse spatial-keyword k-nearest neighbor search," ACM Trans. Database Syst., vol. 39, no. 2, pp. 13:1–13:46, 2014.
  17. S. Basu Roy and K. Chakrabarti, "Location-aware type ahead search on spatial databases: Semantics and efficiency," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2011, pp. 361–372.
  18. R. Zhong, J. Fan, G. Li, K.-L. Tan, and L. Zhou, "Location-aware instant search," in Proc. 21st ACM Conf. Inf. Knowl. Manage., 2012, pp. 385–394.
  19. I. Miliou and A. Vlachou, "Location-aware tag recommendations for flickr," in Proc. 25th Int. Conf. Database Expert Syst. Appl., 2014, pp. 97–104.
  20. H. Tong, C. Faloutsos, and J.-Y. Pan, "Fast random walk with restart and its applications," in Proc. 6th Int. Conf. Data Mining, 2006, pp. 613–622.
  21. Y. Fujiwara, M. Nakatsuji, M. Onizuka, and M. Kitsuregawa, "Fast and exact top-k search for random walk with restart," Proc. VLDB Endowment, vol. 5, no. 5, pp. 442–453, Jan. 2012.
  22. D. Fogaras, B. R_acz, K. Csalog_any, and T. Sarl_os, "Towards scaling fully personalized PageRank: Algorithms, lower bounds, and experiments," Internet Math., vol. 2, no. 3, pp. 333–358, 2005.
  23. B. Bahmani, A. Chowdhury, and A. Goel, "Fast incremental and personalized PageRank," Proc. VLDB Endowment, vol. 4, no. 3, pp. 173–184, Dec. 2010.
  24. K. Avrachenkov, N. Litvak, D. Nemirovsky, E. Smirnova, and M. Sokol, "Quick detection of top-k personalized PageRank lists," in Proc. 8th Int. Workshop Algorithms Models Web Graph, 2011, vol. 6732, pp. 50–61.
  25. P. Berkhin, "Bookmark-coloring algorithm for personalized pagerank computing," Internet Math., vol. 3, pp. 41–62, 2006

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 746-750
Manuscript Number : CSEIT1725112
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

J. V. D Prasad, M. Sri Mounica, "Pattern Evaluation with Location Based Query Search", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.746-750, September-October-2017.
Journal URL :

Article Preview

Follow Us

Contact Us