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

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

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.

langue originale	Anglais
titre	Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	159-165
Nombre de pages	7
ISBN (Electronique)	9781509021789
Les DOIs	https://doi.org/10.1109/BigMM.2016.49
état	Publié - 16 août 2016
Modification externe	Oui
Evénement	2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, Taiwan Durée: 20 avr. 2016 → 22 avr. 2016

Série de publications

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

Une conférence

Une conférence	2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
Pays/Territoire	Taiwan
La ville	Taipei
période	20/04/16 → 22/04/16

Accès au document

10.1109/BigMM.2016.49

Contient cette citation

Hashem, H., & Ranc, D. (2016). Extending standard mapreduce algorithms. Dans Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016 (p. 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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title = "Extending standard mapreduce algorithms",

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.",

keywords = "BigData Tools, Data Modeling, MapReduce Algorithms, Pre-Processing",

author = "Hadi Hashem and Daniel Ranc",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 ; Conference date: 20-04-2016 Through 22-04-2016",

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",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Hashem, H & Ranc, D 2016, Extending standard mapreduce algorithms. Dans 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., p. 159-165, 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016, Taipei, Taiwan, 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).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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T1 - Extending standard mapreduce algorithms

AU - Hashem, Hadi

AU - Ranc, Daniel

PY - 2016/8/16

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N2 - 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.

AB - 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.

KW - BigData Tools

KW - Data Modeling

KW - MapReduce Algorithms

KW - Pre-Processing

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DO - 10.1109/BigMM.2016.49

M3 - Conference contribution

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T3 - Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016

Y2 - 20 April 2016 through 22 April 2016

ER -

Extending standard mapreduce algorithms

Résumé

Série de publications

Une conférence

Accès au document

Empreinte digitale

Contient cette citation