Research Article
Nearest Keyword Multi-Dimensional Data by Index Hashing
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 175 - Issue 3 |
| Published: Oct 2017 |
| Authors: Kavitha Guda, Doolam Ramdarshan |
10.5120/ijca2017915478
|
Kavitha Guda, Doolam Ramdarshan . Nearest Keyword Multi-Dimensional Data by Index Hashing. International Journal of Computer Applications. 175, 3 (Oct 2017), 13-15. DOI=10.5120/ijca2017915478
@article{ 10.5120/ijca2017915478,
author = { Kavitha Guda,Doolam Ramdarshan },
title = { Nearest Keyword Multi-Dimensional Data by Index Hashing },
journal = { International Journal of Computer Applications },
year = { 2017 },
volume = { 175 },
number = { 3 },
pages = { 13-15 },
doi = { 10.5120/ijca2017915478 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2017
%A Kavitha Guda
%A Doolam Ramdarshan
%T Nearest Keyword Multi-Dimensional Data by Index Hashing%T
%J International Journal of Computer Applications
%V 175
%N 3
%P 13-15
%R 10.5120/ijca2017915478
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Catchphrase predicated look for in content prosperous multi-dimensional datasets encourages various novel applications and executes. In this paper, we consider objects that are marked with catchphrases and are embedded in a vector space. For these datasets, we ponder request that demand the most impervious aggregations of centers slaking a given course of action of watchwords. We propose a novel strategy called ProMiSH (Projection and Multi Scale Hashing) that uses self-confident projection and hash-predicated list structures, and achieves high flexibility and speedup. We present a right and an estimated variation of the count. Our exploratory results on sound and produced datasets show that ProMiSH has up to 60 times of speedup over front line tree-predicated frameworks.
References
- W. Li and C. X. Chen, “Efficient data modeling and querying system for multi-dimensional spatial data,” in GIS, 2008, pp. 58:1–58:4.
- D. Zhang, B. C. Ooi, and A. K. H. Tung, “Locating mapped resources in web 2.0,” in ICDE, 2010, pp. 521–532.
- V. Singh, S. Venkatesha, and A. K. Singh, “Geo-clustering of images with missing geotags,” in GRC, 2010, pp. 420–425.
- V. Singh, A. Bhattacharya, and A. K. Singh, “Querying spatial patterns,” in EDBT, 2010, pp. 418–429.
- J. Bourgain, “On lipschitz embedding of finite metric spaces in Hilbert space,” Israel J. Math., vol. 52, pp. 46–52, 1985.
- H. He and A. K. Singh, “Graphrank: Statistical modeling and mining of significant subgraphs in the feature space,” in ICDM, 2006, pp. 885–890.
- X. Cao, G. Cong, C. S. Jensen, and B. C. Ooi, “Collective spatial keyword querying,” in SIGMOD, 2011.
- C. Long, R. C.-W. Wong, K. Wang, and A. W.-C. Fu, “Collective spatial keyword queries: a distance owner-driven approach,” in SIGMOD, 2013.
- D. Zhang, Y. M. Chee, A. Mondal, A. K. H. Tung, and M. Kitsuregawa, “Keyword search in spatial databases: Towards searching by document,” in ICDE, 2009, pp. 688–699.
- M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni, “Locality-sensitive hashing scheme based on p-stable distributions,” in SCG, 2004.
Index Terms
Keywords