 |


















|
|
The Pyramid-Tree: Breaking the Curse of Dimensionality | Full Paper (PDF)
|
In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spaces. The Pyramid-Technique is highly adapted to range query processing using the maximum metric Lmax. In contrast to all other index structures, the performance of the Pyramid-Technique does not deteriorate when processing range queries on data of higher dimensionality. The Pyramid-Technique is based on a special partitioning strategy which is optimized for high-dimensional data. The basic idea is to divide the data space first into 2d pyramids sharing the center point of the space as a top. In a second step, the single pyramids are cut into slices parallel to the basis of the pyramid. These slices form the data pages. Furthermore, we show that this partition provides a mapping from the given d-dimensional space to a 1-dimensional space. Therefore, we are able to use a B+-tree to manage the transformed data. As an analytical evaluation of our technique for hypercube range queries and uniform data distribution shows, the Pyramid-Technique clearly outperforms index structures using other partitioning strategies. To demonstrate the practical relevance of our technique, we experimentally compared the Pyramid-Technique with the X-tree, the Hilbert R-tree, and the Linear Scan. The results of our experiments using both, synthetic and real data, demonstrate that the Pyramid-Technique outperforms the X-tree and the Hilbert R-tree by a factor of up to 14 (number of page accesses) and up to 2500 (total elapsed time) for range queries. |
References, where available, link to the DBLP on the World Wide Web.
[1]Rudolf Bayer, Edward M. McCreight:
Organization and Maintenance of Large Ordered Indices.
Acta Informatica 1: 173-189(1972)[2]Stefan Berchtold, Christian Böhm, Bernhard Braunmüller, Daniel A. Keim, Hans-Peter Kriegel:
Fast Similarity Search in Multimedia Databases.
SIGMOD Conference 1997: 1-12[3]Stefan Berchtold, Christian Böhm, Hans-Peter Kriegel:
Improving the Query Performance of High-Dimensional Index Structures by Bulk-Load Operations.
EDBT 1998: 216-230[4]...
[5]Stefan Berchtold, Christian Böhm, Daniel A. Keim, Hans-Peter Kriegel:
A Cost Model For Nearest Neighbor Search in High-Dimensional Data Space.
PODS 1997: 78-86[6]Stefan Berchtold, Daniel A. Keim, Hans-Peter Kriegel:
The X-tree : An Index Structure for High-Dimensional Data.
VLDB 1996: 28-39[7]Stefan Berchtold, Bernhard Ertl, Daniel A. Keim, Hans-Peter Kriegel, Thomas Seidl:
Fast Nearest Neighbor Search in High-Dimensional Space.
ICDE 1998: 209-218[8]...
[9]Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, Bernhard Seeger:
The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles.
SIGMOD Conference 1990: 322-331[10]Douglas Comer:
The Ubiquitous B-Tree.
Computing Surveys 11(2): 121-137(1979)[11]Surajit Chaudhuri, Umeshwar Dayal:
Data Warehousing and OLAP for Decision Support (Tutorial).
SIGMOD Conference 1997: 507-508[12]Christos Faloutsos, Ron Barber, Myron Flickner, Jim Hafner, Wayne Niblack, Dragutin Petkovic, William Equitz:
Efficient and Effective Querying by Image Content.
JIIS 3(3/4): 231-262(1994)[13]Christos Faloutsos, Pravin Bhagwat:
Declustering Using Fractals.
PDIS 1993: 18-25[14]Jerome H. Friedman, Jon Louis Bentley, Raphael A. Finkel:
An Algorithm for Finding Best Matches in Logarithmic Expected Time.
TOMS 3(3): 209-226(1977)[15]Gisli R. Hjaltason, Hanan Samet:
Ranking in Spatial Databases.
SSD 1995: 83-95[16]H. V. Jagadish:
A Retrieval Technique for Similar Shapes.
SIGMOD Conference 1991: 208-217[17]David A. White, Ramesh Jain:
Similarity Indexing: Algorithms and Performance.
Storage and Retrieval for Image and Video Databases (SPIE) 1996: 62-73[18]Norio Katayama, Shin'ichi Satoh:
The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries.
SIGMOD Conference 1997: 369-380[19]King-Ip Lin, H. V. Jagadish, Christos Faloutsos:
The TV-Tree: An Index Structure for High-Dimensional Data.
VLDB Journal 3(4): 517-542(1994)[20]Rajiv Mehrotra, James E. Gary:
Feature-Based Retrieval of Similar Shapes.
ICDE 1993: 108-115[21]John T. Robinson:
The K-D-B-Tree: A Search Structure For Large Multidimensional Dynamic Indexes.
SIGMOD Conference 1981: 10-18[22]Thomas Seidl, Hans-Peter Kriegel:
Efficient User-Adaptable Similarity Search in Large Multimedia Databases.
VLDB 1997: 506-515[23]David A. White, Ramesh Jain:
Similarity Indexing with the SS-tree.
ICDE 1996: 516-523
|
@inproceedings{DBLP:conf/sigmod/BerchtoldBK98, author = {Stefan Berchtold and Christian B{\"o}hm and Hans-Peter Kriegel}, editor = {Laura M. Haas and Ashutosh Tiwary}, title = {The Pyramid-Tree: Breaking the Curse of Dimensionality}, booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA}, publisher = {ACM Press}, year = {1998}, isbn = {0-89791-955-5}, pages = {142-153}, crossref = {DBLP:conf/sigmod/98}, bibsource = {DBLP, http://dblp.uni-trier.de} }
|
DBLP: Copyright ©1999 by Michael Ley (ley@uni-trier.de).
|
|