Selectivity estimation for spatial joins with geometric selections

Chengyu Sun, Divyakant Agrawal, Amr El Abbadi

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Spatial join is an expensive operation that is commonly used in spatial database systems. In order to generate efficient query plans for the queries involving spatial join operations, it is crucial to obtain accurate selectivity estimates for these operations. In this paper we introduce a framework for estimating the selectivity of spatial joins constrained by geometric selections. The center piece of the framework is Euler Histogram, which decomposes the estimation process into estimations on vertices, edges and faces. Based on the characteristics of different datasets, different probabilistic models can be plugged into the framework to provide better estimation results. To demonstrate the effectiveness of this framework, we implement it by incorporating two existing probabilistic models, and compare the performance with the Geometric Histogram [1] and the algorithm recently proposed by Mamoulis and Papadias [2].

Original languageEnglish
Pages (from-to)609-626
Number of pages18
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2287
DOIs
Publication statusPublished - 1 Jan 2002

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Selectivity estimation for spatial joins with geometric selections'. Together they form a unique fingerprint.

  • Cite this