CFP last date
20 August 2024
Call for Paper
September Edition
IJCA solicits high quality original research papers for the upcoming September edition of the journal. The last date of research paper submission is 20 August 2024

Submit your paper
Know more
Reseach Article

Effectively and Efficiency Consideration for Spatial Database

by Mohammed Otair
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 18
Year of Publication: 2013
Authors: Mohammed Otair

Mohammed Otair . Effectively and Efficiency Consideration for Spatial Database. International Journal of Computer Applications. 74, 18 ( July 2013), 32-37. DOI=10.5120/12987-0232

@article{ 10.5120/12987-0232,
author = { Mohammed Otair },
title = { Effectively and Efficiency Consideration for Spatial Database },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 18 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { },
doi = { 10.5120/12987-0232 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T21:42:39.575279+05:30
%A Mohammed Otair
%T Effectively and Efficiency Consideration for Spatial Database
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 18
%P 32-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA

Metric spaces are very useful in spatial database, and other applications that deal with it. Especially when want to found object that are similar to other object. This condition does not handle the relative position that found in some tree, such that, R-Tree, R+-Tree, and R*-Tree. Instead of handling distances between objects, using Euclidean Distance to compute the similarity between them, and retrieve the sets of object from database based on Euclidean distance that make this situation is happen and occurs. This paper proposed methods for similarity search that make the general assumption in the similarity can be done, by focusing on the effectively and efficiency in metric space for spatial database. This paper introduces how to improve the effectively and efficiency of the spatial database. It provided to extract some knowledge from spatial database has been presented. The main goal is to know how the search occurs, by using some assumption and ensures that there is similarity between a given queries Q and a set of object that found in the database.

  1. Eamonn Keogh, Selina Chu, and Michael Pazzani (2001): A New Approach to Indexing Large Databases
  2. Slobodan Rasetic, Jörg Sander, James Elding Mario A. Nascimento (2005), A Trajectory Splitting Model for Efficient Spatio-Temporal Indexing
  3. Ralf Hartmut GQting (2004),An Introduction to Spatial Database Systems. Received July 25, 1994; accepted August 18, 1994.
  4. TOLGA BOZKAYA, and MERAL OZSOYOGLU (2000). Indexing Large Metric Spaces for Similarity Search Queries
  5. ESSAM A. EL-KWAE , and MANSUR R. KABUKA (2000). Efficient Content-Based Indexing of Large Image Databases.
  6. GISLI R. HJALTASON and HANAN SAMET (2003). Index-Driven Similarity Search in Metric Spaces . ACM Transactions on Database Systems, Vol. 28, No. 4, December 2003, Pages 517–580.
  7. Alock Aggarwal and Jeffery scott Vitter. The input/output complexity of sorting and related problem. Communication of ACM,1988.
  8. Ravidra K. Ahuja, Thomas L. Ma and James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.
  9. http://en. wikipedia. org/wiki/Euclidean_distance,Elena Deza & Michel Marie Deza (2009) Encyclopedia of Distances, page 94, Springer
Index Terms

Computer Science
Information Sciences


Similarity Search Euclidean Distance Compression Nearest Neighbor Query Range Query and Ranking