Spatial query processing for fuzzy objects

Zheng, Kai, Zhou, Xiaofang, Fung, Pui Cheong and Xie, Kexin (2012) Spatial query processing for fuzzy objects. Vldb Journal, 21 5: 729-751. doi:10.1007/s00778-012-0266-x

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Author Zheng, Kai
Zhou, Xiaofang
Fung, Pui Cheong
Xie, Kexin
Title Spatial query processing for fuzzy objects
Journal name Vldb Journal   Check publisher's open access policy
ISSN 1066-8888
Publication date 2012-10
Sub-type Article (original research)
DOI 10.1007/s00778-012-0266-x
Open Access Status
Volume 21
Issue 5
Start page 729
End page 751
Total pages 23
Place of publication New York, NY United States
Publisher Association for Computing Machinery
Collection year 2013
Language eng
Formatted 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.
Keyword Nearest neighbor query
Fuzzy database
Probabilistic database
Closest Pair Problem
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 12 times in Scopus Article | Citations
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