instant fuzzy search engine with phrase based ranking
Kate Sangita Rajendra,Gore Sneha Subhash,Sayyad Laila Mahamud,Solankar Punam Vitthal
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:4 Issue:3 Year: 22 March,2015 Pages:384-392
Abstract
Instant search retrieves results as a user types keyword character by character. On every keystroke result of previously typed prefixed query is used to generate result of newly typed query with one new character. Also to make instant search result computation is done incrementally. Fuzzy search allows user to type query in the fly even he don’t have more information about search. Autocompletion provides suggestions while user types query, it provides way for what to type next. Main problem of retrieving quick result is solved in this paper by using efficient trie data structure with efficient trie search technique. More relevant answers generation is based on phrases, so ranking of result becomes efficient. The records with exact phrases in the query are ranked higher to give more efficient result to user.
Kewords
Proximity Ranking, Autocompletion, Phrases, Fuzzy search, Incremental Computation, Trie, Active Nodes.
Reference
[1]I. Cetindil, J. Esmaelenzhad, C. Li, and D. Newman, “Analysis of instant search query logs,”in W,ebDB,2012,pp.7-12. [2]R. B. Miller, “Response time in man-computer conversational transactions,” in Proceedings of the December 9-11, 1968, fall joint computer conference, part I, ser. AFIPS ’68 (Fall, part I). NewYork, NY, USA: ACM, 1968, pp. 267–277. [3] C. Silverstein, m. R. Henzinger, H. Marais, and M. Moricz, “Analysis of a very large web search engine quary log,” SIGIR Forum. Vol. 33, no. 1, pp. 6-12, 1999. [4] H. Bast and I. Weber, “Type less,find more:fast autocompletion search with a succinct index”,in SIGIR,2006,pp.364-371. [5] S. Ji,G. Li,C. Li,and J. Feng, “Efficient interactive fuzzy keyword search,”in www,2009,pp.371-380. [6] A. Nandi and H. V. Jagadish, “Effective phrase prediction”, in VLDB,2007,pp.219-230. [7] A. Behm. S. Ji. C. Li, and J. Lu, “Space-constrained gram-based indexing for efficient approximate string search”, in ICDE, 2009, pp. 604-615. [8] K. Chakrabarti, S. Chaudhuri, V. Ganti, and D. Xin, “An efficient filter for approximate membership checking”, in SIGMOD Conference, 2008, pp. 805-818. [9] R. Song, M. J. Taylor, J.-R. Wen, H.-W. Hon, and Y. Yu, “Viewing term proximity from a different perspective,” in ECIR, 2008, pp. 346–357. [10] R. Schenkel, A. Broschart, S. won Hwang, M. Theobald, and G. Weikum, “Efficient text proximity search,” in SPIRE, 2007, pp. 287– 299. [11] M. Zhu, S. Shi, M. Li, and J.-R. Wen, “Effective top-k computation in retrieving structured documents with term-proximity support,” in CIKM,2007, pp. 771–780.
