Spatial query processing for fuzzy objects

  • Authors:

  • Kai Zheng;Xiaofang Zhou;Pui Cheong Fung;Kexin Xie

  • Affiliations:

  • School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia 4072;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia 4072 and NICTA Queensland Research Laboratory, Sydney, Australia;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia 4072;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia 4072

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2012

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Abstract

Range and nearest neighbor queries are the most common types of spatial queries, which have been investigated extensively in the last decades due to its broad range of applications. In this paper, we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS communities. Existing research on fuzzy objects mainly focuses on modeling basic fuzzy object types and operations, leaving the processing of more advanced queries largely untouched. In this paper, we propose two new kinds of spatial queries for fuzzy objects, namely single threshold query and continuous threshold query, to determine the query results which qualify at a certain probability threshold and within a probability interval, respectively. For efficient single threshold query processing, we optimize the classical R-tree-based search algorithm by deriving more accurate approximations for the distance function between fuzzy objects and the query object. To enhance the performance of continuous threshold queries, effective pruning rules are developed to reduce the search space and speed up the candidate refinement process. The efficiency of our proposed algorithms as well as the optimization techniques is verified with an extensive set of experiments using both synthetic and real datasets.