Efficient processing of substring match queries with inverted variable-length gram indexes

Kim, Younghoon; Park, Hyoung-Min; Shim, Kyuseok; Woo, Kyoung-Gu

doi:10.1016/j.ins.2013.04.037

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…In this subsection, we compare the performance of the adaptive method with the state-of-the-art methods BWA [13] and APR [10]. Since APR proposed multiple algorithms, we compared with the best one APR-GRQ, which utilizes a greedy algorithm to select v-gram.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…Length filtering, position filtering, prefix filtering and several variant filtering were proposed to find candidates efficiently [8,21,18,1,2]. The second one aims to find substrings similar to a query [15,10,13]. We call it approximate substring matching.…”

Section: Related Workmentioning

confidence: 99%

“…v-gram) index method to improve the performance of approximate string matching [11,23]. Kim et al extended the v-gram index method to support approximate substring matching [10].…”

Section: Related Workmentioning

confidence: 99%

“…Besides, the alphabet size also affects the performance, since it is more likely to have common substrings for a string collection in a small size of alphabet. The second one employs a filter and verification framework [17,10]. The idea is to utilize a filter process to find a group of substrings as candidates, and the final result can be obtained by further verifying the edit distance with the query.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Approach of Approximate Substring Matching

Wang

Yang

Wang

et al. 2016

Database Systems for Advanced Applications

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Approximate substring matching is a common problem in many applications. In this paper, we study approximate substring matching with edit distance constraints. Existing methods are very sensitive to query strings or query parameters like query length and edit distance. To address the problem, we propose a new approach using partition scheme. It first partitions a query into several segments, and finds matching substrings of these segments as candidates, then performs a bidirectional verification on these candidates to get final results. We devise an even partition scheme to efficiently find candidates, and a best partition scheme to find high quality candidates. Furthermore, through theoretical analysis, we find that the best partition scheme cannot always outperform the even partition scheme. Thus we propose an adaptive approach for selectively choosing scheme using statistic knowledge. We conduct comprehensive experiments to demonstrate the efficiency and quality of our proposed method.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…v-gram) index method to improve the performance of approximate string matching [11,23]. Kim et al extended the v-gram index method to support approximate substring matching [10].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Approach of Approximate Substring Matching

Wang

Yang

Wang

et al. 2016

Database Systems for Advanced Applications

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“…Then, by applying set union and intersection operations on signatures, it computes the selectivity of a predicate string. Kim et al [18] used a similar technique to estimate the selectivity of a string query. That is, they used inverted-gram indices to estimate the selectivity of q-grams.…”

Section: Related Workmentioning

confidence: 99%

Estimating the selectivity of LIKE queries using pattern-based histograms

Aytimur¹,

Çakmak²

2018

Turk J Elec Eng & Comp Sci

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Accurate cost and time estimation of a query is one of the major success indicators for database management systems. SQL allows the expression of flexible queries on text-formatted data. The LIKE operator is used to search for a specified pattern (e.g., LIKE "luck%") in a string database. It is vital to estimate the selectivity of such flexible predicates for the query optimizer to choose an efficient execution plan. In this paper, we study the problem of estimating the selectivity of a LIKE query predicate over a bag of strings. We propose a new type of pattern-based histogram structure to summarize the data distribution in a particular column. More specifically, we first mine sequential patterns over a given string database and then construct a special histogram out of the mined patterns. During query optimization time, pattern-based histograms are exploited to estimate the selectivity of a LIKE predicate. The experimental results on a real dataset from DBLP show that the proposed technique outperforms the state of the art for generic LIKE queries like %s1%s2%...%sn% where si represents one or more characters. What is more, the proposed histogram structure requires more than two orders of magnitude smaller memory space, and the estimation time is almost an order of magnitude less in comparison to the state of the art.

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