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Journal articles on the topic 'Approximate String Matching (ASM)'

Author: Grafiati

Published: 6 September 2023

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1

Liu, Bing, Dan Han, and Shuang Zhang. "Approximate Chinese String Matching Techniques Based on Pinyin Input Method." Applied Mechanics and Materials 513-517 (February 2014): 1017–20. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1017.

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String matching is one of the most typical problems in computer science. Previous studies mainly focused on accurate string matching problem. However, with the rapid development of the computer and Internet as well as the continuously rising of new issues, people find that it has very important theoretical value and practical meaning to research and design efficient approximate string matching algorithms. Approximate string matching is also called string matching that allows errors, which mainly aims to find the pattern string in the text and database and allows k differences between the pattern string and its occurring forms in the text. For the problem of approximate string matching, though a number of algorithms have been proposed, there are fewer studies which focus on large size of alphabet . Most of experts are interested in small or middle size of alphabet . For large size of , especially for Chinese characters and Asian phonetics, there are fewer efficient algorithms. For the above reasons, this paper focuses on the approximate Chinese strings matching problem based on the pinyin input method.

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2

Alba, Alfonso, Martin O. Mendez, Miguel E. Rubio-Rincon, and Edgar R. Arce-Santana. "A consensus algorithm for approximate string matching and its application to QRS complex detection." International Journal of Modern Physics C 27, no. 03 (February 23, 2016): 1650029. http://dx.doi.org/10.1142/s0129183116500297.

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In this paper, a novel algorithm for approximate string matching (ASM) is proposed. The novelty resides in the fact that, unlike most other methods, the proposed algorithm is not based on the Hamming or Levenshtein distances, but instead computes a score for each symbol in the search text based on a consensus measure. Those symbols with sufficiently high scores will likely correspond to approximate instances of the pattern string. To demonstrate the usefulness of the proposed method, it has been applied to the detection of QRS complexes in electrocardiographic signals with competitive results when compared against the classic Pan-Tompkins (PT) algorithm. The proposed method outperformed PT in 72% of the test cases, with no extra computational cost.

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3

Yang, Zhenglu, Jianjun Yu, and Masaru Kitsuregawa. "Fast Algorithms for Top-k Approximate String Matching." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1467–73. http://dx.doi.org/10.1609/aaai.v24i1.7527.

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Top-k approximate querying on string collections is an important data analysis tool for many applications, and it has been exhaustively studied. However, the scale of the problem has increased dramatically because of the prevalence of the Web. In this paper, we aim to explore the efficient top-k similar string matching problem. Several efficient strategies are introduced, such as length aware and adaptive q-gram selection. We present a general q-gram based framework and propose two efficient algorithms based on the strategies introduced. Our techniques are experimentally evaluated on three real data sets and show a superior performance.

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4

Mustafa, Suleiman H. "Word-oriented approximate string matching using occurrence heuristic tables: A heuristic for searching Arabic text." Journal of the American Society for Information Science and Technology 56, no. 14 (2005): 1504–11. http://dx.doi.org/10.1002/asi.20244.

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5

Wang, Rui, Ping Gu, and Jian Min Zeng. "A Vague Words Retrieval Method in a Relational Database." Applied Mechanics and Materials 268-270 (December 2012): 1692–96. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1692.

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In this paper, we propose a vague words retrieval method over text field of relational databases. This method is expected to get an ideal retrieval result from text field of relational databases when a set of incorrect keywords is submitted. The solution to this issue is: to create a “hot words library”, then let the input incorrect keywords match with the word of “hot words library”, based on the modified dynamic programming algorithm of k-difference approximate string matching. Finally, Experiments show that this solution has a good query performance.

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6

Baeza-Yates and G. Navarro, R. "Faster Approximate String Matching." Algorithmica 23, no. 2 (February 1999): 127–58. http://dx.doi.org/10.1007/pl00009253.

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7

Owolabi, O., and D. R. McGregor. "Fast approximate string matching." Software: Practice and Experience 18, no. 4 (April 1988): 387–93. http://dx.doi.org/10.1002/spe.4380180407.

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8

Shang, H., and T. H. Merrettal. "Tries for approximate string matching." IEEE Transactions on Knowledge and Data Engineering 8, no. 4 (1996): 540–47. http://dx.doi.org/10.1109/69.536247.

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9

Ukkonen, Esko. "Algorithms for approximate string matching." Information and Control 64, no. 1-3 (January 1985): 100–118. http://dx.doi.org/10.1016/s0019-9958(85)80046-2.

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10

Das, Shibsankar, and Kalpesh Kapoor. "Weighted approximate parameterized string matching." AKCE International Journal of Graphs and Combinatorics 14, no. 1 (April 1, 2017): 1–12. http://dx.doi.org/10.1016/j.akcej.2016.11.010.

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11

Tarhio, Jorma, and Esko Ukkonen. "Approximate Boyer–Moore String Matching." SIAM Journal on Computing 22, no. 2 (April 1993): 243–60. http://dx.doi.org/10.1137/0222018.

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12

Kim, Jong Yong, and John Shawe-Taylor. "An approximate string-matching algorithm." Theoretical Computer Science 92, no. 1 (January 1992): 107–17. http://dx.doi.org/10.1016/0304-3975(92)90138-6.

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13

Russo, Luís M., Gonzalo Navarro, Arlindo Oliveira, and Pedro Morales. "Approximate String Matching with Compressed Indexes." Algorithms 2, no. 3 (September 10, 2009): 1105–36. http://dx.doi.org/10.3390/a2031105.

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14

BERGERON, ANNE, and SYLVIE HAMEL. "VECTOR ALGORITHMS FOR APPROXIMATE STRING MATCHING." International Journal of Foundations of Computer Science 13, no. 01 (February 2002): 53–65. http://dx.doi.org/10.1142/s0129054102000947.

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Vector algorithms allow the computation of an output vector r = r1 r2 ⋯ rm given an input vector e = e1 e2 ⋯ em in a bounded number of operations, independent of m the length of the vectors. The allowable operations are usually restricted to bit-wise operations available in processors, including shifts and binary addition with carry. These restrictions imple that the existence of a vector algorithm for a particular problem opens the way to extremely fast implementations, using the inherent parallelism of bit-wise operations. This paper presents general results on the existence and construction of vertor algorithms, with a particular focus on problems arising from computational biology. We show that efficient vector algorithms exist for the problem of approximate string matching with arbitrary weighted distances, generalizing a previous result by G. Myers. We also characterize a class of automata for which vector algorithms can be automatically derived from the transition table of the automata.

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15

Holub, Jan, and Bořivoj Melichar. "Approximate string matching using factor automata." Theoretical Computer Science 249, no. 2 (October 2000): 305–11. http://dx.doi.org/10.1016/s0304-3975(00)00064-5.

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16

Ukkonen, Esko, and Derick Wood. "Approximate string matching with suffix automata." Algorithmica 10, no. 5 (November 1993): 353–64. http://dx.doi.org/10.1007/bf01769703.

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17

Baeza-Yates, Ricardo A., and Chris H. Perleberg. "Fast and practical approximate string matching." Information Processing Letters 59, no. 1 (July 1996): 21–27. http://dx.doi.org/10.1016/0020-0190(96)00083-x.

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18

Tsur, Dekel. "Fast index for approximate string matching." Journal of Discrete Algorithms 8, no. 4 (December 2010): 339–45. http://dx.doi.org/10.1016/j.jda.2010.08.002.

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19

Chan, Ho-Leung, Tak-Wah Lam, Wing-Kin Sung, Siu-Lung Tam, and Swee-Seong Wong. "Compressed Indexes for Approximate String Matching." Algorithmica 58, no. 2 (December 17, 2008): 263–81. http://dx.doi.org/10.1007/s00453-008-9263-2.

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20

Wright, Alden H. "Approximate string matching using withinword parallelism." Software: Practice and Experience 24, no. 4 (April 1994): 337–62. http://dx.doi.org/10.1002/spe.4380240402.

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21

Ho, ThienLuan, Seung-Rohk Oh, and HyunJin Kim. "New algorithms for fixed-length approximate string matching and approximate circular string matching under the Hamming distance." Journal of Supercomputing 74, no. 5 (November 20, 2017): 1815–34. http://dx.doi.org/10.1007/s11227-017-2192-6.

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22

Barton, Carl, Costas S. Iliopoulos, and Solon P. Pissis. "Fast algorithms for approximate circular string matching." Algorithms for Molecular Biology 9, no. 1 (2014): 9. http://dx.doi.org/10.1186/1748-7188-9-9.

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23

Loo, Mark,P J. ,van,der. "The stringdist Package for Approximate String Matching." R Journal 6, no. 1 (2014): 111. http://dx.doi.org/10.32614/rj-2014-011.

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24

Cole, Richard, and Ramesh Hariharan. "Approximate String Matching: A Simpler Faster Algorithm." SIAM Journal on Computing 31, no. 6 (January 2002): 1761–82. http://dx.doi.org/10.1137/s0097539700370527.

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25

Navarro, Gonzalo. "A guided tour to approximate string matching." ACM Computing Surveys 33, no. 1 (March 2001): 31–88. http://dx.doi.org/10.1145/375360.375365.

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26

Lopresti, Daniel, and Andrew Tomkins. "Block edit models for approximate string matching." Theoretical Computer Science 181, no. 1 (July 1997): 159–79. http://dx.doi.org/10.1016/s0304-3975(96)00268-x.

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27

Rubio, Miguel, Alfonso Alba, Martín Mendez, Edgar Arce-Santana, and Margarita Rodriguez-Kessler. "A Consensus Algorithm for Approximate String Matching." Procedia Technology 7 (2013): 322–27. http://dx.doi.org/10.1016/j.protcy.2013.04.040.

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28

Kucherov, Gregory, Kamil Salikhov, and Dekel Tsur. "Approximate string matching using a bidirectional index." Theoretical Computer Science 638 (July 2016): 145–58. http://dx.doi.org/10.1016/j.tcs.2015.10.043.

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29

Landau, Gad M., and Uzi Vishkin. "Fast parallel and serial approximate string matching." Journal of Algorithms 10, no. 2 (June 1989): 157–69. http://dx.doi.org/10.1016/0196-6774(89)90010-2.

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30

Akutsu, Tatsuya. "Approximate string matching with don't care characters." Information Processing Letters 55, no. 5 (September 1995): 235–39. http://dx.doi.org/10.1016/0020-0190(95)00111-o.

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31

Chang, W. I., and E. L. Lawler. "Sublinear approximate string matching and biological applications." Algorithmica 12, no. 4-5 (November 1994): 327–44. http://dx.doi.org/10.1007/bf01185431.

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32

Navarro, Gonzalo, and Ricardo Baeza-Yates. "Very fast and simple approximate string matching." Information Processing Letters 72, no. 1-2 (October 1999): 65–70. http://dx.doi.org/10.1016/s0020-0190(99)00121-0.

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33

Huynh, Trinh N. D., Wing-Kai Hon, Tak-Wah Lam, and Wing-Kin Sung. "Approximate string matching using compressed suffix arrays." Theoretical Computer Science 352, no. 1-3 (March 2006): 240–49. http://dx.doi.org/10.1016/j.tcs.2005.11.022.

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34

Navarro, Gonzalo, and Edgar Chávez. "A metric index for approximate string matching." Theoretical Computer Science 352, no. 1-3 (March 2006): 266–79. http://dx.doi.org/10.1016/j.tcs.2005.11.037.

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35

Amir, Amihood, Yonatan Aumann, Oren Kapah, Avivit Levy, and Ely Porat. "Approximate string matching with address bit errors." Theoretical Computer Science 410, no. 51 (November 2009): 5334–46. http://dx.doi.org/10.1016/j.tcs.2009.09.010.

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36

Amir, Amihood, Estrella Eisenberg, Orgad Keller, Avivit Levy, and Ely Porat. "Approximate string matching with stuck address bits." Theoretical Computer Science 412, no. 29 (July 2011): 3537–44. http://dx.doi.org/10.1016/j.tcs.2011.02.044.

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37

Hon, Wing-Kai, Tak-Wah Lam, Rahul Shah, Siu-Lung Tam, and Jeffrey Scott Vitter. "Cache-oblivious index for approximate string matching." Theoretical Computer Science 412, no. 29 (July 2011): 3579–88. http://dx.doi.org/10.1016/j.tcs.2011.03.004.

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38

Atallah, M. J., F. Chyzak, and P. Dumas. "A Randomized Algorithm for Approximate String Matching." Algorithmica 29, no. 3 (March 2001): 468–86. http://dx.doi.org/10.1007/s004530010062.

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39

Galil, Zvi, and Kunsoo Park. "An Improved Algorithm For Approximate String Matching." SIAM Journal on Computing 19, no. 6 (December 1990): 989–99. http://dx.doi.org/10.1137/0219067.

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40

Ravi, K. M., A. Choubey, and K. K. Tripati. "Intuitionistic Fuzzy Automaton for Approximate String Matching." Fuzzy Information and Engineering 6, no. 1 (March 2014): 29–39. http://dx.doi.org/10.1016/j.fiae.2014.06.003.

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41

Bille, Philip. "Faster Approximate String Matching for Short Patterns." Theory of Computing Systems 50, no. 3 (April 1, 2011): 492–515. http://dx.doi.org/10.1007/s00224-011-9322-y.

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42

JOKINEN, PETTERI, JORMA TARHIO, and ESKO UKKONEN. "A Comparison of Approximate String Matching Algorithms." Software: Practice and Experience 26, no. 12 (December 1996): 1439–58. http://dx.doi.org/10.1002/(sici)1097-024x(199612)26:12<1439::aid-spe71>3.0.co;2-1.

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43

Aygun, Ramazan S. "Using Maximum Subarrays for Approximate String Matching." Annals of Data Science 4, no. 4 (July 19, 2017): 503–31. http://dx.doi.org/10.1007/s40745-017-0117-0.

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44

Kılınç, Deniz. "An accurate toponym-matching measure based on approximate string matching." Journal of Information Science 42, no. 2 (June 29, 2015): 138–49. http://dx.doi.org/10.1177/0165551515590097.

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45

Ho, ThienLuan, Seung-Rohk Oh, and HyunJin Kim. "Correction to: New algorithms for fixed-length approximate string matching and approximate circular string matching under the Hamming distance." Journal of Supercomputing 74, no. 5 (March 20, 2018): 1835. http://dx.doi.org/10.1007/s11227-018-2324-7.

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46

Mola, Sebastianus A. S., Meiton Boru, and Emerensye Sofia Yublina Pandie. "PEMBOBOTAN DINAMIS BERBASIS POSISI PADA APPROXIMATE STRING MATCHING." Jurnal Komputer dan Informatika 9, no. 2 (October 13, 2021): 168–75. http://dx.doi.org/10.35508/jicon.v9i2.5149.

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Komunikasi tertulis dalam media sosial yang menekankan pada kecepatan penyebaran informasi sering kali terjadi fenomena penggunaan bahasa yang tidak baku baik pada level kalimat, klausa, frasa maupun kata. Sebagai sebuah sumber data, media sosial dengan fenomena ini memberikan tantangan dalam proses ekstraksi informasi. Normalisasi bahasa yang tidak baku menjadi bahasa baku dimulai pada proses normalisasi kata di mana kata yang tidak baku (non-standard word (NSW)) dinormalisasikan ke bentuk baku (standard word (SW)). Proses normalisasi dengan menggunakan edit distance memiliki keterbatasan dalam proses pembobotan nilai mismatch, match, dan gap yang bersifat statis. Dalam perhitungan nilai mismatch, pembobotan statida tidak dapat memberikan pembedaan bobot akibat kesalahan penekanan tombol pada keyboard terutama tombol yang berdekatan. Karena keterbatasan pembobotan edit distance ini maka dalam penelitian ini diusulkan sebuah metode pembobotan dinamis untuk bobot mismatch. Hasil dari penelitian ini adalah adanya metode baru dalam pembobotan dinamis berbasis posisi tombol keyboard yang dapat digunakan dalam melakukan normalisasi NSW menggunakan metode approximate string matching.

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47

Sastry, R., N. Ranganathan, and K. Remedios. "CASM: a VLSI chip for approximate string matching." IEEE Transactions on Pattern Analysis and Machine Intelligence 17, no. 8 (1995): 824–30. http://dx.doi.org/10.1109/34.400575.

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48

Kärkkäinen, Juha, Gonzalo Navarro, and Esko Ukkonen. "Approximate string matching on Ziv–Lempel compressed text." Journal of Discrete Algorithms 1, no. 3-4 (June 2003): 313–38. http://dx.doi.org/10.1016/s1570-8667(03)00032-7.

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49

Michailidis, Panagiotis D., and Konstantinos G. Margaritis. "Processor array architectures for flexible approximate string matching." Journal of Systems Architecture 54, no. 1-2 (January 2008): 35–54. http://dx.doi.org/10.1016/j.sysarc.2007.03.004.

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50

Farach-Colton, Martin, Gad M. Landau, S. Cenk Sahinalp, and Dekel Tsur. "Optimal spaced seeds for faster approximate string matching." Journal of Computer and System Sciences 73, no. 7 (November 2007): 1035–44. http://dx.doi.org/10.1016/j.jcss.2007.03.007.

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