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Mathematical Problems in Engineering
Volume 2012, Article ID 310328, 17 pages
Research Article

FACC: A Novel Finite Automaton Based on Cloud Computing for the Multiple Longest Common Subsequences Search

1School of Computer Science and Technology, Xidian University, Xi'an 710071, China
2School of Software, Xidian University, Xi'an 710071, China

Received 14 April 2012; Accepted 30 August 2012

Academic Editor: Hailin Liu

Copyright © 2012 Yanni Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Searching for the multiple longest common subsequences (MLCS) has significant applications in the areas of bioinformatics, information processing, and data mining, and so forth, Although a few parallel MLCS algorithms have been proposed, the efficiency and effectiveness of the algorithms are not satisfactory with the increasing complexity and size of biologic data. To overcome the shortcomings of the existing MLCS algorithms, and considering that MapReduce parallel framework of cloud computing being a promising technology for cost-effective high performance parallel computing, a novel finite automaton (FA) based on cloud computing called FACC is proposed under MapReduce parallel framework, so as to exploit a more efficient and effective general parallel MLCS algorithm. FACC adopts the ideas of matched pairs and finite automaton by preprocessing sequences, constructing successor tables, and common subsequences finite automaton to search for MLCS. Simulation experiments on a set of benchmarks from both real DNA and amino acid sequences have been conducted and the results show that the proposed FACC algorithm outperforms the current leading parallel MLCS algorithm FAST-MLCS.