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Parallel Processing Systems for Big Data: A Survey
State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing.
State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing.
State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing.
Advanced Computer Systems Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing.
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Number of Authors: 72016 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 104, no 11, p. 2114-2136Article in journal (Refereed) Published
Abstract [en]

The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). MapReduce pioneered this paradigm change and rapidly became the primary big data processing system for its simplicity, scalability, and fine-grain fault tolerance. However, compared with DBMSs, MapReduce also arouses controversy in processing efficiency, low-level abstraction, and rigid dataflow. Inspired by MapReduce, nowadays the big data systems are blooming. Some of them follow MapReduce's idea, but with more flexible models for general-purpose usage. Some absorb the advantages of DBMSs with higher abstraction. There are also specific systems for certain applications, such as machine learning and stream data processing. To explore new research opportunities and assist users in selecting suitable processing systems for specific applications, this survey paper will give a high-level overview of the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category. As the pioneer, the original MapReduce system, as well as its active variants and extensions on dataflow, data access, parameter tuning, communication, and energy optimizations will be discussed at first. System benchmarks and open issues for big data processing will also be studied in this survey.

Place, publisher, year, edition, pages
IEEE, 2016. Vol. 104, no 11, p. 2114-2136
Keywords [en]
Big data; machine learning; MapReduce; parallel processing; SQL; survey
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-30610DOI: 10.1109/JPROC.2016.2591592ISI: 000386244000005Scopus ID: 2-s2.0-84983084815Local ID: 479125cc-1090-471f-836d-760b2c532796OAI: oai:DiVA.org:ltu-30610DiVA, id: diva2:1003839
Note

Validerad; 2016; Nivå 2; 2016-08-31 (inah)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-11-23Bibliographically approved

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Vasilakos, Athanasios

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