Abstract
The R-tree family index structures are among the most common index structures used in multidimensional databases. To improve the search performance it is very important to reduce the overlap between bounding regions in the R-tree. However the arbitrary insertion order in the tree construction procedure might result in tree structures inefficient in the search operations. In this paper we propose a new technique called Hierarchical Clustering-Merging (HCM) to improve the tree construction procedure of the R-tree family index structures. With this technique we can take advantage of the data distribution information in the data set to achieve an optimized tree structure and improve the search performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
O. Guttman, “R-tree: A Dynamic Index Structure for Spatial Searching”, in Proc. ACM SIGMOD, pp.47–57, 1984
N. Katayama and S. Satoh, “The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries”, In Proc. of the ACM SIGMOD, pp. 369–380, 1997
N. Beckmann, H.P. Kriegel, R. Schneider, B. Seeger, “The R*-tree: An Efficient and Robust Access Method for Points and Rectangles”, In Proc. of ACM SIGMOD International Conference on Management of Data, pp. 322–331, May 1990
T. Sellis, N. Roussopoulos, and C. Faloutsos, “The R+-tree: A dynamic index for multi-dimensional objects,” in Proc. of 13th Conf. Very Large Databases, Brighton, U.K., pp. 507–518, Sept. 1987
I. Kamel, C. Faloutsos, “On Packing R-trees”, in Proc. of the 2nd International Conference on Information and Knowledge Management, pp. 490–499, Arlington, VA, November 1993
Roussopoulos, D. Leifker, “Direct Spatial Search on Pictorial Databases Using Packed R-trees”, in Proc. of ACM SIGMOD International Conference on Management of Data, pp. 17–31, 1985
Scott T. Leutenegger et al, “STR: A Simple and Efficient Algorithm for R-tree Packing”, in Proc. of the 13rd IEEE International Conference on Data Engineering, pp. 497–506, Birmingham U.K., 1997
Ng and J. Han, “Efficient and Effective Clustering Method for Spatial Data Mining”, VLDB’94, pp. 144–155, Santiago, Chile, Sept. 1994
A.K. Jian, M.N. Murty, and P. J. Flynn, “Data clustering: A review”, ACM Computing Sur-veys, vol. 31, no. 3, September 1999
G. Lu, “Techniques and data structures for efficient multimedia retrieval based on similarity”, pp. 372–384, IEEE Transactions on Multimedia, Vol. 4, No. 3, Sept. 2002
Open Source Clustering Software: http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/index.html
Source code of multi-dimensional indexing techniques: http://dias.cti.gr/~ytheod/research/indexing/
The SR-tree: http://research.nii.ac.jp/~katayama/homepage/research/srtree/English.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Z., Ding, J., Zhang, M., Tavanapong, W., Wong, J.S. (2003). Hierarchical Clustering-Merging for Multidimensional Index Structures. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_9
Download citation
DOI: https://doi.org/10.1007/3-540-45113-7_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40634-1
Online ISBN: 978-3-540-45113-6
eBook Packages: Springer Book Archive