String correction using the Damerau-Levenshtein distance

Zhao, Chenhao; Sahni, Sartaj

doi:10.1186/s12859-019-2819-0

Cited by 51 publications

(55 citation statements)

References 16 publications

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“…This observation has also been made in [2]. In [18] we developed algorithms that run in O(mn) time using only O(s * min{m, n} + m + n) space, where s is the size of the alphabet comprising the strings, to compute the DL distance as well as the corresponding edit sequence. Since s << m and s << n in most applications (e.g., s = 20 for protein sequences), this reduction in space enables the solution of much larger instances than is possible using the algorithm of [17].…”

Section: Introductionmentioning

confidence: 66%

“…In this paper, we develop algorithms to compute the DL distance and corresponding edit sequence using O(m + n) space and O(mn) time. Extensive experimentation using 3 different platforms indicates that the algorithms of this paper are also faster than those of [18]. In fact, our fastest algorithm for the DL distance is up to 56.4% faster than the fastest algorithm in [18] when run on a single core.…”

Section: Introductionmentioning

confidence: 87%

“…Since s << m and s << n in most applications (e.g., s = 20 for protein sequences), this reduction in space enables the solution of much larger instances than is possible using the algorithm of [17]. Our algorithms in [18] are much faster as well. In this paper, we develop algorithms to compute the DL distance and corresponding edit sequence using O(m + n) space and O(mn) time.…”

Section: Introductionmentioning

confidence: 99%

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Linear space string correction algorithm using the Damerau-Levenshtein distance

Zhao

Sahni

2020

BMC Bioinformatics

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Background The Damerau-Levenshtein (DL) distance metric has been widely used in the biological science. It tries to identify the similar region of DNA,RNA and protein sequences by transforming one sequence to the another using the substitution, insertion, deletion and transposition operations. Lowrance and Wagner have developed an O(mn) time O(mn) space algorithm to find the minimum cost edit sequence between strings of length m and n, respectively. In our previous research, we have developed algorithms that run in O(mn) time using only O(s∗min{m,n}+m+n) space, where s is the size of the alphabet comprising the strings, to compute the DL distance as well as the corresponding edit sequence. These are so far the fastest and most space efficient algorithms. In this paper, we focus on the development of algorithms whose asymptotic space complexity is linear. Results We develop linear space algorithms to compute the Damerau-Levenshtein (DL) distance between two strings and determine the optimal trace (corresponding edit operations.)Extensive experiments conducted on three computational platforms–Xeon E5 2603, I7-x980 and Xeon E5 2695–show that, our algorithms, in addition to using less space, are much faster than earlier algorithms. Conclusion Besides using less space than the previously known algorithms,significant run-time improvement was seen for our new algorithms on all three of our experimental platforms. On all platforms, our linear-space cache-efficient algorithms reduced run time by as much as 56.4% and 57.4% in respect to compute the DL distance and an optimal edit sequences compared to previous algorithms. Our multi-core algorithms reduced the run time by up to 59.3% compared to the best previously known multi-core algorithms.

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Section: Introductionmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Linear space string correction algorithm using the Damerau-Levenshtein distance

Zhao

Sahni

2020

BMC Bioinformatics

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“…The second method is based on pattern analysis for the estimation of the "similarity" of the individual sections of the PRS. In order to estimate each section at each position of the generated PRS, the Damerau-Levenshtein distance 26,27 was calculated. The Damerau-Levenshtein distance is a measure of the difference between two strings of characters, defined as the minimum number of operations such as inserting, deleting, replacing, and transposing (replacement of two adjacent characters) required to turn one string into another.…”

Section: Methodsmentioning

confidence: 99%

Influence of multiplexing conditions on artefact signal and the signal-to-noise ratio in the decoded data in Hadamard transform ion mobility spectrometry

Sarycheva

Adamov

Poteshin

et al. 2020

Eur J Mass Spectrom (Chichester)

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In Hadamard transform ion mobility spectrometry (HT IMS), the signal-to-noise ratio is always lower for non-modified pseudorandom sequences than for modified sequences. Since the use of non-modified modulating pseudorandom sequences is strategically preferable from a duty cycle standpoint, we investigated the change in the interference signal when transitioning from non-modified modulating sequences to sequences modified by the addition of 1,3,5 and 7 zeros. The interfering signal in HT IMS with modified pseudorandom sequences was shown to be mainly random noise for all the cases except for modifying by incorporation of 1 zero. For standard samples of tetraalkylammonium halides, modulation by non-modified pseudorandom sequences is beneficial in the case of small numbers of averaged spectra (below ∼40 averaged spectra compared to any modified pseudorandom sequences except for 1 zero modified and below ∼200 averaged spectra compared to signal averaging ion mobility spectrometry) and worsens the signal-to-noise ratio in the case of large numbers of averaged spectra. Contrarily, modulation by modified pseudorandom sequences is beneficial for any number of averaged spectra, except for very small ones (below 15 averaged spectra compared to modulation by non-modified sequences). Pseudorandom sequence modified with 1 zero incorporation is beneficial in the case of below ∼400 averaged spectra compared to any modified and non-modified pseudorandom sequences. The signal-to-noise ratio in conventional signal averaging mode ion mobility spectrometry is affected by random noise, whereas the HT IMS with non-modified pseudorandom sequences was demonstrated to be primarily affected by a systematic noise-like artefact signal. Because noise-like artefact signals were found to be reproducible, predicting models for interference signals could be generated to improve signal-to-noise ratio. This is significant because non-modified modulating sequences are limited by their poor signal-to-noise ratio. This improvement would increase the viability of non-modified modulating sequences which are preferred because of their higher sample utilization efficiency.

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“…Levenshtein distance is the smallest number of insertions, deletion, and substitution processes that change a word or string to be another string [24]. For example, Levenshtein Distance of string "synthesis" and "synthesize" is 2 because there are two operations: change character 's' into 'z' and addition of character 'e'.…”

Section: Levenshtein Distancementioning

confidence: 99%

Structural and Semantic Similarity Measurement of UML Use Case Diagram

Arifin

Siahaan

2020

LKJITI

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Reusing software has several benefits ranging from reducing cost and risk, accelerating development, and its primary purposes are improving software quality. In the early stage of software development, reusing existing software artifacts may increase the benefit of reusing software because it uses mature artifacts from previous artifacts. One of software artifacts is diagram, and in order to assist the reusing diagram is to find the level of similarity of diagrams. This paper proposes a method for measuring the similarity of the use case diagram using structural and semantic aspects. For structural similarity measurement, Graph Edit Distance is used by transforming each factor and use case into a graph, while for semantic similarity measurement, WordNet, WuPalmer,and Levenshtein were used. The experimentation was conducted on ten datasets from variousprojects. The results of the method were compared with the results of assessments from experts.The measurement of agreement between experts and method was done by using Gwet’s AC1 andPearson correlation coefficient. Measurement results with Gwet’s AC1 diagram similarity are 0,60,which were categorized as “moderate" agreement and the result of measurement with Pearsonis 0.506 which means there is a significant correlation between experts and methods. The resultshowed that the proposed method can be used to find the similarity of the diagram, so finding andreuse of the diagram as a software component can be optimized.

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String correction using the Damerau-Levenshtein distance

Cited by 51 publications

References 16 publications

Linear space string correction algorithm using the Damerau-Levenshtein distance

Linear space string correction algorithm using the Damerau-Levenshtein distance

Influence of multiplexing conditions on artefact signal and the signal-to-noise ratio in the decoded data in Hadamard transform ion mobility spectrometry

Structural and Semantic Similarity Measurement of UML Use Case Diagram

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