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The Longest Common Subsequence(LCS) identification of biological sequences has significant applications in bioinformatics. Due to the emerging growth in bioinformatics applications, new biological sequences with longer length have been used for processing, making it great challenge for sequential LCS algorithms. Few parallel LCS algorithms have been proposed but their efficiency and effectiveness are not satisfactory with increasing complexity and size of biological data. To overcome limitations of existing LCS algorithms and considering MapReduce programming model as promising technology for cost effective high performance parallel computing, MapReduce based parallel algorithm for LCS has been developed. This approach adopts the concepts of successor tables, identical character pairs, successor tree and traversal of successor tree to find Longest Common Subsequence. The hadoop framework is used for the realization of MapReduce model.
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MapReduce Based Approach to Longest Common Subsequence in BioSequences
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LAP LAMBERT Academic Publishing Mär 2015, 2015
ISBN 10: 3659680508
ISBN 13: 9783659680502
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Longest Common Subsequence(LCS) identification of biological sequences has significant applications in bioinformatics. Due to the emerging growth in bioinformatics applications, new biological sequences with longer length have been used for processing, making it great challenge for sequential LCS algorithms. Few parallel LCS algorithms have been proposed but their efficiency and effectiveness are not satisfactory with increasing complexity and size of biological data. To overcome limitations of existing LCS algorithms and considering MapReduce programming model as promising technology for cost effective high performance parallel computing, MapReduce based parallel algorithm for LCS has been developed. This approach adopts the concepts of successor tables, identical character pairs, successor tree and traversal of successor tree to find Longest Common Subsequence. The hadoop framework is used for the realization of MapReduce model. 56 pp. Englisch. Codice articolo 9783659680502
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MapReduce Based Approach to Longest Common Subsequence in BioSequences
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659680508
ISBN 13: 9783659680502
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bohara JnaneshwarMr. Bohara,Computer Engineer at Government of Nepal, is the university topper in M.Sc. in Computer System and Knowledge Engineering at Institute of Engineering(IOE),Tribhuwan University. He worked for 5 years as Sr J. Codice articolo 21942183
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MapReduce Based Approach to Longest Common Subsequence in BioSequences
Editore:
LAP LAMBERT Academic Publishing Mär 2015, 2015
ISBN 10: 3659680508
ISBN 13: 9783659680502
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The Longest Common Subsequence(LCS) identification of biological sequences has significant applications in bioinformatics. Due to the emerging growth in bioinformatics applications, new biological sequences with longer length have been used for processing, making it great challenge for sequential LCS algorithms. Few parallel LCS algorithms have been proposed but their efficiency and effectiveness are not satisfactory with increasing complexity and size of biological data. To overcome limitations of existing LCS algorithms and considering MapReduce programming model as promising technology for cost effective high performance parallel computing, MapReduce based parallel algorithm for LCS has been developed. This approach adopts the concepts of successor tables, identical character pairs, successor tree and traversal of successor tree to find Longest Common Subsequence. The hadoop framework is used for the realization of MapReduce model.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. Codice articolo 9783659680502
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MapReduce Based Approach to Longest Common Subsequence in BioSequences
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659680508
ISBN 13: 9783659680502
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The Longest Common Subsequence(LCS) identification of biological sequences has significant applications in bioinformatics. Due to the emerging growth in bioinformatics applications, new biological sequences with longer length have been used for processing, making it great challenge for sequential LCS algorithms. Few parallel LCS algorithms have been proposed but their efficiency and effectiveness are not satisfactory with increasing complexity and size of biological data. To overcome limitations of existing LCS algorithms and considering MapReduce programming model as promising technology for cost effective high performance parallel computing, MapReduce based parallel algorithm for LCS has been developed. This approach adopts the concepts of successor tables, identical character pairs, successor tree and traversal of successor tree to find Longest Common Subsequence. The hadoop framework is used for the realization of MapReduce model. Codice articolo 9783659680502
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MapReduce Based Approach to Longest Common Subsequence in BioSequences
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659680508
ISBN 13: 9783659680502
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. MapReduce Based Approach to Longest Common Subsequence in BioSequences | Jnaneshwar Bohara (u. a.) | Taschenbuch | 56 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659680502 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 104759262
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