Intelligent Predictive String Search Algorithm

Gurung, Dipendra; Chakraborty, Uday K.; Sharma, Pratikshya

doi:10.1016/j.procs.2016.03.116

Cited by 12 publications

(23 citation statements)

References 1 publication

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“…The Boyer-Moore algorithm becomes one of the most frequently used string-lookup algorithms or is implemented into a document or data search feature in the database because it is considered the most efficient in typical applications and is best compared to other string search algorithms [24,25]. The Boyer-Moore algorithm starts matching a character from the right direction of the pattern or the right-to-left direction of the text [16,26,27]. Adequately systematic, the stages that the Boyer-Moore algorithm performed at the time of matching the following strings [26,28]: a. Boyer-Moore's algorithm starts matching the pattern at the beginning of the text.…”

Section: Boyer-moore Algorithmmentioning

confidence: 99%

“…The KMP algorithm has a different shift than the Boyer-Moore algorithm. Broadly speaking the stages in the KMP algorithm, when performing string matching are [16,20,22,30]: a. Algorithms KMP starts matching pattern at the beginning of the text b. The KMP algorithm performs a shift or matching pattern character with text characters from left to right by matching characters per character until one of the conditions is met.…”

Section: Knuth Morris Pratt Algorithm (Kmp)mentioning

confidence: 99%

“…The boundary value is calculated based on a character pattern only. If anyone appreciates the character in the pattern, then the edge value 1 and so on will increase in value with the value j, and I shift to the next pattern character [16,20,22,30]. Table 4.…”

Section: Knuth Morris Pratt Algorithm (Kmp)mentioning

confidence: 99%

“…The Horspool algorithm is one of the string search algorithms, which is a simplification of the Boyer-Moore algorithm [15,16,31,32]. The Horspool algorithm has a simpler shifting compared to the Boyer-Moore algorithm.…”

Section: Horspool Algorithmmentioning

confidence: 99%

“…The purpose of using a string matching algorithm, also, to speed up the search process also aims to obtain the accuracy of search results. Several algorithms are belonging to this string algorithm, including the Boyer-Moore algorithm, Horspool algorithm, Knuth Morris Pratt algorithm, and others [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of search algorithms in Javanese-Indonesian dictionary application

et al. 2020

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This study aims to compare the performance of Boyer-Moore, Knuth morris pratt, and Horspool algorithms in searching for the meaning of words in the Java-Indonesian dictionary search application in terms of accuracy and processing time. Performance Testing is used to test the performance of algorithm implementations in applications. The test results show that the Boyer Moore and Knuth Morris Pratt algorithms have an accuracy rate of 100%, and the Horspool algorithm 85.3%. While the processing time, Knuth Morris Pratt algorithm has the highest average speed level of 25ms, Horspool 39.9 ms, while the average speed of the Boyer Moore algorithm is 44.2 ms. While the complexity test results, the Boyer Moore algorithm has an overall number of n 26n 2 , Knuth Morris Pratt and Horspool 20n 2 each.

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Section: Boyer-moore Algorithmmentioning

confidence: 99%

Section: Knuth Morris Pratt Algorithm (Kmp)mentioning

confidence: 99%

Section: Knuth Morris Pratt Algorithm (Kmp)mentioning

confidence: 99%

Section: Horspool Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of search algorithms in Javanese-Indonesian dictionary application

et al. 2020

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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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