MBR compression in spatial databases using semi-approximation scheme

Authors:
Jongwan Kim;SeokJin Im;Sang-Won Kang;SeongHoon Lee;Chong-Sun Hwang
Affiliations:
Department of Computer Science and Engineering, Korea University, Seoul, Korea;Department of Computer Science and Engineering, Korea University, Seoul, Korea;Department of Computer Science and Engineering, Korea University, Seoul, Korea;Division of Information and Communication, Cheonan University, Cheonan, Korea;Department of Computer Science and Engineering, Korea University, Seoul, Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

Citing 8
Cited 0

The SR-tree: an index structure for high-dimensional nearest neighbor queries

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Optimizing multidimensional index trees for main memory access

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
CSIM19: CSIM19: a powerful tool for building system models

Proceedings of the 33nd conference on Winter simulation
R-trees: a dynamic index structure for spatial searching

SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Compressing Relations and Indexes

ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
The X-tree: An Index Structure for High-Dimensional Data

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Spatial indexing of high-dimensional data based on relative approximation

The VLDB Journal — The International Journal on Very Large Data Bases
A spatial index using MBR compression and hashing technique for mobile map service

DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Studies on spatial index, which is used for location-based services in mobile computing or GIS have increased in proportion to the increase in the spatial data. However, these studies were on the indices based on R-tree, and there are a few studies on how to increase the search performance of the spatial data by compressing MBRs. This study was conducted in order to propose a new MBR compression scheme, SA (Semi-approximation), and a SAR-tree that indexes spatial data using R-tree. The basic idea of this paper is the compression of MBRs in a spatial index. Since SA decreases the size of MBR keys, halves QMBR enlargement, and increases node utilization. Therefore, the SAR-tree heightens the overall search performance. The experiments show that the proposed index has increased performance, higher than that of the pre-established schemes on compression of MBRs.