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  1. Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems (DISCS-2013)
  2. Novel parallel method for mining frequent patterns on multi-core shared memory systems
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SDAFT: a novel scalable data access framework for parallel BLAST
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CRUCIBLE: towards unified secure on- and off-line analytics at scale
Large data and computation in a hazard map workflow using Hadoop and Neteeza architectures
Enhancing both fairness and performance using rate-aware dynamic storage cache partitioning
Novel parallel method for mining frequent patterns on multi-core shared memory systems
A framework for an in-depth comparison of scale-up and scale-out
Design of an active storage cluster file system for DAG workflows
Toward a data scalable solution for facilitating discovery of scientific data resources
BDMPI: conquering BigData with small clusters using MPI

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Novel parallel method for mining frequent patterns on multi-core shared memory systems

Content Provider ACM Digital Library
Author Vu, Lan Alaghband, Gita
Abstract Frequent pattern mining is an important problem in data mining with many practical applications. Current parallel methods for mining frequent patterns unstably perform for different database types and under-utilize the benefits of multi-core shared memory machines. We present ShaFEM, a novel parallel frequent pattern mining method, to address these issues. Our method can dynamically adapt to the data characteristics to efficiently perform on both sparse and dense databases. Its parallel mining lock free approach minimizes the synchronization needs and maximizes the data independence to enhance the scalability. Its structure lends itself well for dynamic job scheduling resulting in well-balanced load on new multi-core shared memory architectures. We evaluate ShaFEM on a 12-core multi-socket server and find that our method runs 2.1--5.8 times faster than the state-of-the-art parallel method. For some test cases, we have shown that ShaFEM saves 4.9 days and 12.8 hours of execution time over the compared method.
Starting Page 49
Ending Page 54
Page Count 6
File Format PDF
ISBN 9781450325066
DOI 10.1145/2534645.2534653
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-18
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Association rule mining Parallel algorithm Databases Multi-core Frequent pattern mining Shared memory
Content Type Text
Resource Type Article
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