A. K, D. S, and V. Rajan. Imperial Journal of Interdisciplinary Research, (2017)
Abstract
Keyword-based search in text-rich multi-dimensional datasets facilitates many novel applications and tools. In this paper, the objects that are tagged with keywords are embedded in a vector space. For these datasets, queries where asked for the tightest groups of points satisfying a given set of keywords. A novel method called ProMiSH (Projection and Multi Scale Hashing) which uses random projection and hash-based index structures, and achieves high scalability and speedup. Thus, the paper introduces an exact and an approximate version of the algorithms.
Description
Survey on Multi-Dimensional Datasets | K | Imperial Journal of Interdisciplinary Research
%0 Journal Article
%1 k2017survey
%A K, Anju
%A S, Dhanasree K
%A Rajan, Vincy
%D 2017
%J Imperial Journal of Interdisciplinary Research
%K datasets dimensional multi survey
%N 8
%T Survey on Multi-Dimensional Datasets
%U http://www.imperialjournals.com/index.php/IJIR/article/view/5463
%V 3
%X Keyword-based search in text-rich multi-dimensional datasets facilitates many novel applications and tools. In this paper, the objects that are tagged with keywords are embedded in a vector space. For these datasets, queries where asked for the tightest groups of points satisfying a given set of keywords. A novel method called ProMiSH (Projection and Multi Scale Hashing) which uses random projection and hash-based index structures, and achieves high scalability and speedup. Thus, the paper introduces an exact and an approximate version of the algorithms.
@article{k2017survey,
abstract = { Keyword-based search in text-rich multi-dimensional datasets facilitates many novel applications and tools. In this paper, the objects that are tagged with keywords are embedded in a vector space. For these datasets, queries where asked for the tightest groups of points satisfying a given set of keywords. A novel method called ProMiSH (Projection and Multi Scale Hashing) which uses random projection and hash-based index structures, and achieves high scalability and speedup. Thus, the paper introduces an exact and an approximate version of the algorithms. },
added-at = {2017-08-25T09:40:13.000+0200},
author = {K, Anju and S, Dhanasree K and Rajan, Vincy},
biburl = {https://www.bibsonomy.org/bibtex/2249b2245fb569534d62a066c086cf16b/ijirjournal},
description = {Survey on Multi-Dimensional Datasets | K | Imperial Journal of Interdisciplinary Research},
id = {5463},
interhash = {fc3b8661cd5ddd47726a1e8f2fbb71f5},
intrahash = {249b2245fb569534d62a066c086cf16b},
issn = {2454-1362},
journal = {Imperial Journal of Interdisciplinary Research},
keywords = {datasets dimensional multi survey},
number = 8,
source = {Imperial Journal of Interdisciplinary Research},
timestamp = {2017-08-25T09:40:13.000+0200},
title = {Survey on Multi-Dimensional Datasets},
type = {Text.Serial.Journal},
uri = {http://www.imperialjournals.com/index.php/IJIR},
url = {http://www.imperialjournals.com/index.php/IJIR/article/view/5463},
volume = 3,
year = 2017
}