A cost model for similarity queries in metric spaces
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The String-to-String Correction Problem
Journal of the ACM (JACM)
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
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Communications of the ACM
A guided tour to approximate string matching
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Exploratory image databases: content-based retrieval
Exploratory image databases: content-based retrieval
ACM Computing Surveys (CSUR)
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IEEE Transactions on Knowledge and Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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DEXA '97 Proceedings of the 8th International Workshop on Database and Expert Systems Applications
Distance Exponent: A New Concept for Selectivity Estimation in Metric Trees
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Retrieval by shape similarity with perceptual distance andeffective indexing
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Fast business process similarity search with feature-based similarity estimation
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SC-tree: an efficient structure for high-dimensional data indexing
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Searching in a large data set those strings that are more similar, according to the edit distance, to a given one is a time-consuming process. In this paper we investigate the performance of metric trees, namely the M-tree, when they are extended using a cheap approximate distance function as a filter to quickly discard irrelevant strings. Using the bag distance as an approximation of the edit distance, we show an improvement in performance up to 90% with respect to the basic case. This, along with the fact that our solution is independent on both the distance used in the pre-test and on the underlying metric index, demonstrates that metric indices are a powerful solution, not only for many modern application areas, as multimedia, data mining and pattern recognition, but also for the string matching problem.