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Quick-motif: An efficient and scalable framework for exact motif discovery
Yuhong Li1; Leong Hou U1; Man Lung Yiu2; Zhiguo Gong1
Conference NameInternational Conference on Data Engineering
Source PublicationProceedings - International Conference on Data Engineering
Conference Date13-17 April 2015
Conference PlaceSeoul, Korea (South)

Discovering motifs in sequence databases has been receiving abundant attentions from both database and data mining communities, where the motif is the most correlated pair of subsequences in a sequence object. Motif discovery is expensive for emerging applications which may have very long sequences (e.g., million observations per sequence) or the queries arrive rapidly (e.g., per 10 seconds). Prior works cannot offer fast correlation computations and prune subsequence pairs at the same time, as these two techniques require different orderings on examining subsequence pairs. In this work, we propose a novel framework named Quick-Motif which adopts a two-level approach to enable batch pruning at the outer level and enable fast correlation calculation at the inner level. We further propose two optimization techniques for the outer and the inner level. In our experimental study, our method is up to 3 orders of magnitude faster than the state-of-the-art methods.

URLView the original
Indexed ByCPCI-S
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000519724500051
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Cited Times [WOS]:22   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Affiliation1.Department of Computer and Information Science, University of Macau Av. Padre Tomas Pereira Taipa, Macau
2.Department of Computing, Hong Kong Polytechnic University
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yuhong Li,Leong Hou U,Man Lung Yiu,et al. Quick-motif: An efficient and scalable framework for exact motif discovery[C],2015:579-590.
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