An Efficient Pattern Matching Algorithm

Sleit, Azzam; Almobaideen, Wesam; Baarah, Aladdin; Abusitta, Adel

doi:10.3923/jas.2007.2691.2695

Cited by 13 publications

(10 citation statements)

References 10 publications

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“…Boyer-Moore algorithm 9 The Boyer-Moore (BM) approach [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] is frequently used in the software industry. The right-to-left scan, the bmBc shift rule, and the bmGs shift rule are three ingenious concepts used by the Boyer-Moore algorithm.…”

Section: 11mentioning

confidence: 99%

“…Quick search (QS) 6,10,13,15,16,20,25,27,29,[32][33][34] is an implication of the BM algorithm that employs solely the bmBc shift. The (qsBc) shift table is used in the QSalgorithm to record each character's shift value in the pattern.…”

Section: Quick Search Algorithmmentioning

confidence: 99%

“…The BR algorithm was developed in 1999 by Berry and Ravindran. The Berry-Ravindran (BR) algorithm 15,16,20,21,42,43 computes shift values by taking the bad character shift for two consecutive text characters (a substring) in the T to the right of the window into account. The shift values are calculated using the (brBc) function and an array of two-dimensional.…”

Section: Berry-ravindran Algorithmmentioning

confidence: 99%

“…During the preprocessing stage, the shift value is calculated for each letter in the alphabet set. 15,25,34 As a result, the approach is useful in practical scenarios when the alphabet length is high but the pattern size is short. 14,46 2.1.…”

Section: The Horspool Algorithmmentioning

confidence: 99%

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A survey on improving pattern matching algorithms for biological sequences

Hamed

Ibrahim

El‐Hafeez

2022

Concurrency and Computation

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Summary Pattern matching is a highly useful procedure in several stages of the computational pipelines. Furthermore, some research trends in this research domain contributed to growing biological databases and updated them throughout time. This article proposes an comparison and analysis of different algorithms for match equivalent pattern matching like complexity, efficiency, and techniques. Which algorithm is best for which DNA sequence and why? This describes the different algorithms for various activities that include pattern matching as an important aspect of functionality. This article shows that BM, Horspool, ZT, QS, FS, Smith, and SSABS methods employ the bad character preprocessing function. In addition, BM, SSABS, TVSBS, and BRFS methods are using two approaches in the preprocessing stage, which decreases the preprocessing time. Furthermore, KR, QS, SSABS, BRFS, and Shift‐Or are not recommended for the long pattern, whereas ZT, FS, d‐BM, Raita, and Smith are not recommended for the short pattern. This is because they are time‐consuming and certain algorithms, such as ZT and DCPM, use a lot of time and space during the matching and search process, while others, such as d‐BM and TSW, save space and time. Although DCPM, BRFS, and QS are quicker than other algorithms, FLPM, PAPM, and LFPM rank highest in terms of complexity time.

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Section: 11mentioning

confidence: 99%

Section: Quick Search Algorithmmentioning

confidence: 99%

Section: Berry-ravindran Algorithmmentioning

confidence: 99%

Section: The Horspool Algorithmmentioning

confidence: 99%

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A survey on improving pattern matching algorithms for biological sequences

Hamed

Ibrahim

El‐Hafeez

2022

Concurrency and Computation

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“…Many applications will benefit from efficient spatial data information retrieval and search query results. Applications either in real life or online that are using spatial or spatiotemporal extensions of the data gathered require -in a timely manner -an efficient processing of such huge and complex data, in order to extract meaningful information [12] and to support the new challenges imposed by the diversity of spatial data. Building scalable indexes for spatial data will serve the aim of facing the challenges of fast indexing and information retrieval of spatial data [8].…”

Section: Introductionmentioning

confidence: 99%

SOLD: A node-Splitting algorithm for R-tree based on Objects’ Locations Distribution

Al-Nsour

Sleit

Alshraideh

2018

Journal of Information Science

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Spatial data indexing methods are of extreme importance as they massively build up as a result of the explosive growth in capturing data with spatial features. No matter how much the data size is, eventually it will reside on disk pages. Disk pages have to be properly indexed to preserve spatial properties of objects, optimise disk space usage and improve objects’ retrieval performance. One of the most popular spatial data indexes is the R-tree which is a height balanced tree data structure, where leaf nodes resemble disk pages and contain pointers to objects’ locations. A single tree node can host up to a maximum number of objects, where any more insertion makes it an overflown node and it has to be split. Better splits lead to better index performance and more utilisation of disk space. In this work, we introduce a new way of finding the most proper split for an overflown node in the R-tree index. The proposed work scans – in a linear cost – the overflown node’s objects once to identify the distribution of objects’ locations (minimum bounding rectangles (MBRs)) in relative to its node’s bounding rectangle (node’s MBR). It uses objects’ locations to calculate – for each main axis – the split quality factors: expected overlap between resulting nodes, objects distribution evenness among resulting nodes and the perimeter of resulting nodes. The axis with better combined quality factors values is selected as the split axis. The Splitting based on Objects’ Locations Distribution (SOLD) algorithm was implemented and tested against two other splitting algorithms, experiments using synthetic and real data files showed good results and it outperformed both algorithms in index creation tests and data retrieval tests.

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