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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 545-556
Bin Yao , Computer Science Department, Florida State University, Tallahassee, USA
Feifei Li , Computer Science Department, Florida State University, Tallahassee, USA
Marios Hadjieleftheriou , AT&T Labs Research, Florham Park, NJ, USA
Kun Hou , Computer Science Department, Florida State University, Tallahassee, USA
ABSTRACT
This work presents a novel index structure, MHR-tree, for efficiently answering approximate string match queries in large spatial databases. The MHR-tree is based on the R-tree augmented with the min-wise signature and the linear hashing technique. The min-wise signature for an index node u keeps a concise representation of the union of q-grams from strings under the sub-tree of u. We analyze the pruning functionality of such signatures based on set resemblance between the query string and the q-grams from the sub-trees of index nodes. MHR-tree supports a wide range of query predicates efficiently, including range and nearest neighbor queries. We also discuss how to estimate range query selectivity accurately. We present a novel adaptive algorithm for finding balanced partitions using both the spatial and string information stored in the tree. Extensive experiments on large real data sets demonstrate the efficiency and effectiveness of our approach.
CITATION
Bin Yao, Feifei Li, Marios Hadjieleftheriou, Kun Hou, "Approximate string search in spatial databases", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 545-556, doi:10.1109/ICDE.2010.5447836
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