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Implementation of X-Tree Structure for Multidimensional Data Ranking

by Kavitha Raju, S. K. Srivatsa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 7
Year of Publication: 2012
Authors: Kavitha Raju, S. K. Srivatsa
10.5120/8903-2931

Kavitha Raju, S. K. Srivatsa . Implementation of X-Tree Structure for Multidimensional Data Ranking. International Journal of Computer Applications. 56, 7 ( October 2012), 26-29. DOI=10.5120/8903-2931

@article{ 10.5120/8903-2931,
author = { Kavitha Raju, S. K. Srivatsa },
title = { Implementation of X-Tree Structure for Multidimensional Data Ranking },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 7 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number7/8903-2931/ },
doi = { 10.5120/8903-2931 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:13.203036+05:30
%A Kavitha Raju
%A S. K. Srivatsa
%T Implementation of X-Tree Structure for Multidimensional Data Ranking
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 7
%P 26-29
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this modern world, travelling has become as normal routine for most of the people in the cities. At the same time, frequency of happening of accidents also increasing alarmingly. Also one cannot avoid health care and should take proper health checks in hospitals periodically. At the time of accidents, to save precious lives, we must know better hospitals nearby. In that context, in this paper X-tree [1] structure has been implemented for multidimensional data ranking. Problem definition is explained in section1, importance of X-tree is explained in section2, Proposed System explained in section3, Advantages of Proposed system explained in Section 4, Implementation and sample data are explained in sections 5 and 6, module description and other constraints have been explained in the remaining sections.

References
  1. A. Stefan Berchtold Daniel, The X-tree: An Index Structure for High-Dimensional Data, Keim Hans-Peter Kriegei Institute for Computer Science, University of Munich, Oettingenstr. 67, D-80538 Munich, Germany.
  2. M. L. Yiu, X. Dai, N. Mamoulis, and M. Vaitis, "Top-k Spatial Preference Queries," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2007.
  3. G. R. Hjaltason and H. Samet, "Distance Browsing in Spatial Databases," ACM Trans. Database Systems, vol. 24, no. 2, pp. 265-318, 1999.
  4. R. Weber, H. J. Schek, and S. Blott, "A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces," Proc. Int'l Conf. Very Large Data Bases (VLDB), 1998.
  5. K. S. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, "When is 'Nearest Neighbor' Meaningful?" Proc. Seventh International Conference Database Theory (ICDT), 1999.
  6. I. F. Ilyas, W. G. Aref, and A. Elmagarmid, "Supporting Top-k Join Queries in Relational Databases," Proc. 29th Int'l Conf. Very Large Data Bases (VLDB), 2003.
  7. D. Papadias, P. Kalnis, J. Zhang, and Y. Tao, "Efficient OLAP Operations in Spatial Data Warehouses," Proc. Int'l Symp. Spatial and Temporal Databases (SSTD), 2001.
Index Terms

Computer Science
Information Sciences

Keywords

X-tree data ranking quality ranking

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