Extending standard mapreduce algorithms

Hadi Hashem; Daniel Ranc

doi:10.1109/BigMM.2016.49

Extending standard mapreduce algorithms

Hadi Hashem
, Daniel Ranc

CNRS SAMOVAR UMR 5157

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The information rate nowadays is expanding very quickly and contains complex and heterogeneous data types (text, images, videos, GPS data, purchase transactions) that require powerful computing engines, able to easily store and process such complex structures. Gartner's definition of the 3Vs (volume, velocity, variety) describing this expansion of data will then lead to extract the unnamed forth V (value) from BigData. This added value addresses the need for valuation of enterprise data. In this paper, we discuss the existing MapReduce implementation techniques and the need of a different approach based on the pre-processing of the data. The goal is to show interesting results in terms of data processing costs, performance and green computing.

Original language	English
Title of host publication	Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	159-165
Number of pages	7
ISBN (Electronic)	9781509021789
DOIs	https://doi.org/10.1109/BigMM.2016.49
Publication status	Published - 16 Aug 2016
Externally published	Yes
Event	2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, Taiwan, Province of China Duration: 20 Apr 2016 → 22 Apr 2016

Publication series

Name	Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

Conference

Conference	2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
Country/Territory	Taiwan, Province of China
City	Taipei
Period	20/04/16 → 22/04/16

Keywords

BigData Tools
Data Modeling
MapReduce Algorithms
Pre-Processing

Access to Document

10.1109/BigMM.2016.49

Cite this

Hashem, H., & Ranc, D. (2016). Extending standard mapreduce algorithms. In Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016 (pp. 159-165). Article 7545014 (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigMM.2016.49

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author = "Hadi Hashem and Daniel Ranc",

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year = "2016",

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doi = "10.1109/BigMM.2016.49",

language = "English",

series = "Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016",

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Hashem, H & Ranc, D 2016, Extending standard mapreduce algorithms. in Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016., 7545014, Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 159-165, 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016, Taipei, Taiwan, Province of China, 20/04/16. https://doi.org/10.1109/BigMM.2016.49

Extending standard mapreduce algorithms. / Hashem, Hadi; Ranc, Daniel.
Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 159-165 7545014 (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - Pre-Processing

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Extending standard mapreduce algorithms

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Keywords

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