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    ACME: A scalable parallel system for extracting frequent patterns from a very long sequence

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    Type
    Article
    Authors
    Sahli, Majed cc
    Mansour, Essam
    Kalnis, Panos cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2014-10-02
    Online Publication Date
    2014-10-02
    Print Publication Date
    2014-12
    Permanent link to this record
    http://hdl.handle.net/10754/563784
    
    Metadata
    Show full item record
    Abstract
    Modern applications, including bioinformatics, time series, and web log analysis, require the extraction of frequent patterns, called motifs, from one very long (i.e., several gigabytes) sequence. Existing approaches are either heuristics that are error-prone, or exact (also called combinatorial) methods that are extremely slow, therefore, applicable only to very small sequences (i.e., in the order of megabytes). This paper presents ACME, a combinatorial approach that scales to gigabyte-long sequences and is the first to support supermaximal motifs. ACME is a versatile parallel system that can be deployed on desktop multi-core systems, or on thousands of CPUs in the cloud. However, merely using more compute nodes does not guarantee efficiency, because of the related overheads. To this end, ACME introduces an automatic tuning mechanism that suggests the appropriate number of CPUs to utilize, in order to meet the user constraints in terms of run time, while minimizing the financial cost of cloud resources. Our experiments show that, compared to the state of the art, ACME supports three orders of magnitude longer sequences (e.g., DNA for the entire human genome); handles large alphabets (e.g., English alphabet for Wikipedia); scales out to 16,384 CPUs on a supercomputer; and supports elastic deployment in the cloud.
    Citation
    Sahli, M., Mansour, E., & Kalnis, P. (2014). ACME: A scalable parallel system for extracting frequent patterns from a very long sequence. The VLDB Journal, 23(6), 871–893. doi:10.1007/s00778-014-0370-1
    Publisher
    Springer Nature
    Journal
    The VLDB Journal
    DOI
    10.1007/s00778-014-0370-1
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00778-014-0370-1
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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