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Approximate string matching

About: Approximate string matching is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 62352 citations. The topic is also known as: fuzzy string-searching algorithm & fuzzy string-matching algorithm.


Papers
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01 Jan 1999
TL;DR: It is argued that, in addition to previous applications that required such search, multi-pattern matching can be used in lieu of indexed or sorted data in some applications involving small to medium size datasets.
Abstract: A new algorithm to search for multiple patterns at the same time is presented. The algorithm is faster than previous algorithms and can support a very large number — tens of thousands — of patterns. Several applications of the multi-pattern matching problem are discussed. We argue that, in addition to previous applications that required such search, multi-pattern matching can be used in lieu of indexed or sorted data in some applications involving small to medium size datasets. Its advantage, of course, is that no additional search structure is needed.

564 citations

Proceedings Article
11 Sep 2001
TL;DR: In this article, the authors propose a technique for building approximate string join capabilities on top of commercial databases by exploiting facilities already available in them. But this technique relies on matching short substrings of length, called -grams, and taking into account both positions of individual matches and the total number of such matches.
Abstract: String data is ubiquitous, and its management has taken on particular importance in the past few years. Approximate queries are very important on string data especially for more complex queries involving joins. This is due, for example, to the prevalence of typographical errors in data, and multiple conventions for recording attributes such as name and address. Commercial databases do not support approximate string joins directly, and it is a challenge to implement this functionality efficiently with user-defined functions (UDFs). In this paper, we develop a technique for building approximate string join capabilities on top of commercial databases by exploiting facilities already available in them. At the core, our technique relies on matching short substrings of length , called -grams, and taking into account both positions of individual matches and the total number of such matches. Our approach applies to both approximate full string matching and approximate substring matching, with a variety of possible edit distance functions. The approximate string match predicate, with a suitable edit distance threshold, can be mapped into a vanilla relational expression and optimized by conventional relational optimizers. We demonstrate experimentally the benefits of our technique over the direct use of UDFs, using commercial database systems and real data. To study the I/O and CPU behavior of approximate string join algorithms with variations in edit distance and -gram length, we also describe detailed experiments based on a prototype implementation.

556 citations

01 Jan 2003
TL;DR: An open-source Java toolkit of methods for matching names and records is described and results obtained from using various string distance metrics on the task of matching entity names are summarized.
Abstract: We describe an open-source Java toolkit of methods for matching names and records. We summarize results obtained from using various string distance metrics on the task of matching entity names. These metrics include distance functions proposed by several different communities, such as edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. We then describe an extension to the toolkit which allows records to be compared. We discuss some issues involved in performing a similar comparision for record-matching techniques, and finally present results for some baseline record-matching algorithms that aggregate string comparisons between fields.

552 citations

Proceedings ArticleDOI
09 Jun 2003
TL;DR: A new similarity function is proposed which overcomes limitations of commonly used similarity functions, and an efficient fuzzy match algorithm is developed which can effectively clean an incoming tuple if it fails to match exactly with any tuple in the reference relation.
Abstract: To ensure high data quality, data warehouses must validate and cleanse incoming data tuples from external sources. In many situations, clean tuples must match acceptable tuples in reference tables. For example, product name and description fields in a sales record from a distributor must match the pre-recorded name and description fields in a product reference relation.A significant challenge in such a scenario is to implement an efficient and accurate fuzzy match operation that can effectively clean an incoming tuple if it fails to match exactly with any tuple in the reference relation. In this paper, we propose a new similarity function which overcomes limitations of commonly used similarity functions, and develop an efficient fuzzy match algorithm. We demonstrate the effectiveness of our techniques by evaluating them on real datasets.

548 citations

01 Jan 1974
TL;DR: By exploiting the formal similarity of string-matching with integer multiplication, a new algorithm has been obtained with a running time which is only slightly worse than linear.
Abstract: : The string-matching problem considered is to find all occurrences of a given pattern as a substring of another longer string. When the pattern is simply a given string of symbols, there is an algorithm due to Morris, Knuth and Pratt which has a running time proportional to the total length of the pattern and long string together. This time may be achieved even on a Turing machine. The more difficult case where either string may have don't care symbols which are deemed to match with all symbols is also considered. By exploiting the formal similarity of string-matching with integer multiplication, a new algorithm has been obtained with a running time which is only slightly worse than linear. (Author)

488 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202230
202132
202030
201948
201839