Efficient Merging and Filtering Algorithms for Approximate String Searches

Efficient Merging and Filtering Algorithms for Approximate String Searches

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by Chen Li , Jiaheng Lu , Yiming Lu

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Datum	Value	Source
TITLE	Efficient Merging and Filtering Algorithms for Approximate String Searches	SVM HeaderParse 0.2
AUTHOR NAME	Chen Li	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, University of California, Irvine	SVM HeaderParse 0.2
AUTHOR ADDR	CA 92697, USA	SVM HeaderParse 0.2
AUTHOR NAME	Jiaheng Lu	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, University of California, Irvine	SVM HeaderParse 0.2
AUTHOR ADDR	CA 92697, USA	SVM HeaderParse 0.2
AUTHOR NAME	Yiming Lu	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, University of California, Irvine	SVM HeaderParse 0.2
AUTHOR ADDR	CA 92697, USA	SVM HeaderParse 0.2
ABSTRACT	Abstract — We study the following problem: how to efficiently find in a collection of strings those similar to a given query string? Various similarity functions can be used, such as edit distance, Jaccard similarity, and cosine similarity. This problem is of great interests to a variety of applications that need a high real-time performance, such as data cleaning, query relaxation, and spellchecking. Several algorithms have been proposed based on the idea of merging inverted lists of grams generated from the strings. In this paper we make two contributions. First, we develop several algorithms that can greatly improve the performance of existing algorithms. Second, we study how to integrate existing filtering techniques with these algorithms, and show that they should be used together judiciously, since the way to do the integration can greatly affect the performance. We have conducted experiments on several real data sets to evaluate the proposed techniques. I.	SVM HeaderParse 0.2
CITATIONS	9 found	ParsCit 1.0