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

Handle URI:
http://hdl.handle.net/10754/563784
Title:
ACME: A scalable parallel system for extracting frequent patterns from a very long sequence
Authors:
Sahli, Majed ( 0000-0002-4576-9708 ) ; Mansour, Essam; Kalnis, Panos ( 0000-0002-5060-1360 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Springer Nature
Journal:
The VLDB Journal
Issue Date:
2-Oct-2014
DOI:
10.1007/s00778-014-0370-1
Type:
Article
ISSN:
10668888
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSahli, Majeden
dc.contributor.authorMansour, Essamen
dc.contributor.authorKalnis, Panosen
dc.date.accessioned2015-08-03T12:09:58Zen
dc.date.available2015-08-03T12:09:58Zen
dc.date.issued2014-10-02en
dc.identifier.issn10668888en
dc.identifier.doi10.1007/s00778-014-0370-1en
dc.identifier.urihttp://hdl.handle.net/10754/563784en
dc.description.abstractModern 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.en
dc.publisherSpringer Natureen
dc.subjectAutomatic tuningen
dc.subjectCache efficienten
dc.subjectClouden
dc.subjectElasticen
dc.subjectMotifen
dc.subjectSuffix treeen
dc.titleACME: A scalable parallel system for extracting frequent patterns from a very long sequenceen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalThe VLDB Journalen
dc.contributor.institutionQatar Computing Research InstituteDoha, Qataren
kaust.authorKalnis, Panosen
kaust.authorSahli, Majeden
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