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- W1999916788 abstract "We study efficient query processing for approximate string queries, which find strings within a string collection whose edit distances to the query strings are within the given thresholds. Existing methods typically hinge on the property that globally similar strings must share at least certain number of identical substrings or subsequences. They become ineffective when there are burst errors or when the number of errors is large. In this paper, we explore the opposite paradigm focusing on finding out the differences of database strings to the query string. We propose a new filtering method, called local filtering, based on the idea that two strings exhibiting substantial local dissimilarities must be globally dissimilar. We propose the concept of (positional) local distance to quantify the minimum amount of errors a query fragment contributes to the edit distance between the query and a data string. It also leads to effective pruning rules and can speed up verification via early termination. We devise a family of indexing methods based on the idea of precomputing (positional) local distances for all possible combinations of query fragments and edit distance thresholds. Based on careful analyses of subtle relationships among local distances, novel techniques are proposed to drastically reduce the amount of enumeration with no or little impact on the pruning power. Efficient query processing methods exploiting the new index and bit-parallelism are also proposed. Experimental results on real datasets show that our local filtering-based methods can achieve substantial speedup compared with state-of-the-art methods, and they are robust against factors such as dataset characteristics and large edit distance thresholds." @default.
- W1999916788 created "2016-06-24" @default.
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- W1999916788 date "2015-05-27" @default.
- W1999916788 modified "2023-09-22" @default.
- W1999916788 title "Local Filtering" @default.
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- W1999916788 doi "https://doi.org/10.1145/2723372.2749445" @default.
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