Summarizing level-two topological relations in large spatial datasets

  • Authors:

  • Xuemin Lin;Qing Liu;Yidong Yuan;Xiaofang Zhou;Hongjun Lu

  • Affiliations:

  • NICTA & University of New South Wales, Sydney, Australia;NICTA & University of New South Wales, Sydney, Australia;NICTA & University of New South Wales, Sydney, Australia;University of Queensland, Australia;Hong Kong University of Science and Technology, Hong Kong

  • Venue:
  • ACM Transactions on Database Systems (TODS)
  • Year:
  • 2006

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Abstract

Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.