Locality of Corner Transformation for Multidimensional Spatial Access Methods

Authors:
H. K. Dai;K. -Y. Whang;H. C. Su
Affiliations:
Computer Science Department, Oklahoma State University, Stillwater, Oklahoma 74078, U.S.A.;Division of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Republic of Korea;Department of Computer Science, Arkansas State University, State University, Arkansas 72467, U.S.A.
Venue:
Electronic Notes in Theoretical Computer Science (ENTCS)
Year:
2008

Citing 4
Cited 0

Hausdorff distance under translation for points and balls

Proceedings of the nineteenth annual symposium on Computational geometry
Transform-Space View: Performing Spatial Join in the Transform Space Using Original-Space Indexes

IEEE Transactions on Knowledge and Data Engineering
Adaptive row major order: a new space filling curve for efficient spatial join processing in the transform space

Journal of Systems and Software
On p-norm based locality measures of space-filling curves

ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The geometric structural complexity of spatial objects does not render an intuitive distance metric on the data space that measures spatial proximity. However, such a metric provides a formal basis for analytical work in transformation-based multidimensional spatial access methods, including locality preservation of the underlying transformation and distance-based spatial queries. We study the Hausdorff distance metric on the space of multidimensional polytopes, and prove a tight relationship between the metric on the original space of k-dimensional hyperrectangles and the standard p-normed metric on the transform space of 2k-dimensional points under the corner transformation, which justifies the effectiveness of the transformation-based technique in preserving spatial locality.