CFP last date
20 March 2024
Reseach Article

A Comparative Study on the Performance of HZR+ Trees on Query Processing

by Suba S
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 13
Year of Publication: 2014
Authors: Suba S
10.5120/15272-3910

Suba S . A Comparative Study on the Performance of HZR+ Trees on Query Processing. International Journal of Computer Applications. 87, 13 ( February 2014), 42-46. DOI=10.5120/15272-3910

@article{ 10.5120/15272-3910,
author = { Suba S },
title = { A Comparative Study on the Performance of HZR+ Trees on Query Processing },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 13 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number13/15272-3910/ },
doi = { 10.5120/15272-3910 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:52.065216+05:30
%A Suba S
%T A Comparative Study on the Performance of HZR+ Trees on Query Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 13
%P 42-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data is being produced in new forms and unimaginable quantities. Researches and other scientific and commercial applications are engrossing the scientific community for their size and need of faster accessibility. The conventional access methods previously available in multidimensional databases are no longer suitable for the new form of data produced. In traditional databases, multicolumn index is created using B-tree [5]. This indexing cannot slide over columns, so the primary index column must be in the WHERE clause filters of the query. The R-tree [3], an extension of the B-tree, is a hierarchical, height balanced multidimensional indexing structure that guarantees space utilization above a certain threshold. But the data produced in most of the cases are not spatial in nature. Therefore, the data should be restructured in order to map the non-spatial data to geometric space. Thus, the multidimensional accessibility of spatial access methods, experimented on non spatial data for the first time and the analysis of which has produced interesting results forms the major contributions of this paper. The sequence of procedures followed to arrive at the analytical study is as follows: 1. The packing of non spatial data converts the data into a form that paves the way for multidimensional access, similar to using spatial access methods for spatial data. 2. The proposal of reduction of overlap of data using Hilbert curves for ordering the data before insertion into the proposed indexing structure 3. The proposal of a new index structure, Hilbert ZR+ Tree [HZR+ Tree]. 4. A collection of experiments and analysis which validates and proves the efficiency of the proposed data model.

References
  1. Chang-Tien Lu, Jing Dai, Ying Jin, Mathuria J. "GLIP: A Concurrency Control Protocol for Clipping Indexing", Knowledge and Data Engineering, IEEE Transactions, Volume: 21, Issue: 5, May 2009.
  2. Ibrahim Kamel and Christos Faloutsos,Hilbert RTree-An improved Rtree using fractals, Proceedings of the 20th VLDB Conference Santiago, Chlle, September,1994
  3. A. Guttman, "R-Trees: A Dynamic Index Structure for Spatial Searching," Proc. ACM SIGMOD '84, pp. 47-57, 1984.
  4. V. Gaede and O. Gunther, "Multi- dimensional Access Methods", ACM Computing Surveys, Vol 30, no. 2, pp. 170-231, June 1998.
  5. A. Guttman, "R-Trees: A Dynamic Index Structure for Spatial Searching," Proc. ACM SIGMOD '84, pp. 47-57, 1984.
  6. Harry Leslie, Rohit Jain, Dave Birdsall, and Hedieh Yaghmai "Efficient Search of Multidimensional B-Trees", Proceedinge of the 21st VLDB Conference, Zurich, Switzerland, 1995.
  7. T. Sellis, N. Roussopoulos, and C. Faloutsos, "The R+-Tree: A Dynamic Index for Multi-dimensional Objects", Proc. 13th International Conference, Very Large Data Bases (VLDB '87), pp. 507-518, 1987.
  8. Norbert Beckmann, Hans–Peter Kriegel, Ralf Schneider, Bernhard Seeger, Praktuche Informatlk, Umversltaet Bremen, "The R*-tree:An Efficient and Robust access Access Method for Points and Rectangles+", SIGMOD conference, 1990.
  9. Kamel, Ibrahim and Faloutsos, Christos, "On packing R-trees" (1993). Computer Science Department. Paper 588, Conference proceedings of CIKM,January,1993.
  10. N. Roussopoulos ,D. Leifker "Direct spatial searche on pictorial databases using packed R Trees", proc. Of ACM SIGMOD '85
  11. H. V. Jagadish, L. V. S. Lakshmanan, and D. Srivastava. "Snakes and sandwiches: optimal clustering strategies for a data warehouse, SIGMOD Rec. , 28(2):37–48. ACM Press, 1999.
  12. Joseph M. Hellerstein, Jeffrey F. Naughton, "Generalized Search Trees for Database Systems", Proceeding VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases, Zurich, Switzerland, 1995.
  13. Joseph M. Hellerstein, Jeffrey F. Naughton, "Generalized Search Trees for Database Systems", Proceeding VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases.
Index Terms

Computer Science
Information Sciences

Keywords

Hilbert ZR+ Tree packing-data NSR-Tree