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2014 Second International Symposium on Computing and Networking (CANDAR) (2014)
Shizuoka, Japan
Dec. 10, 2014 to Dec. 12, 2014
ISBN: 978-1-4799-4152-0
pp: 28-31
ABSTRACT
MapReduce framework has emerged as one of the most widely used parallel computing platforms for processing Big Data on tera- and peta-byte scale. In this note, we introduce several theoretical computational models for MapReduce from a standpoint of parallel algorithmic power by comparing MapReduce computation with standard parallel computational models such as PRAMs and/or combinational Boolean circuits.
INDEX TERMS
Computational modeling, Phase change random access memory, Memory management, Integrated circuit modeling, Polynomials, Program processors, Tin
CITATION

K. Wada, "Computational Models for Big Data Processing," 2014 Second International Symposium on Computing and Networking (CANDAR), Shizuoka, Japan, 2014, pp. 28-31.
doi:10.1109/CANDAR.2014.40
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