Grand challenge: MapReduce-style processing of fast sensor data

Kasper Grud Skat Madsen; Li Su; Yongluan Zhou

doi:10.1145/2488222.2488281

Grand challenge: MapReduce-style processing of fast sensor data

Kasper Grud Skat Madsen
, Li Su
, Yongluan Zhou

Institut for Matematik og Datalogi

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

Abstract

MapReduce is a popular scalable processing framework for large-scale data. In this paper, we first briefly present our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. We investigated how to utilize such a MapReduce-style platform for fast sensor data processing by taking the DEBS Grand Challenge 2013 as an example. Both the analysis and experiments verify that our approach can obtain highly scalable solutions.

Originalsprog	Engelsk
Titel	DEBS’13 : Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems Arlington, TX, USA — June 29 - July 03, 2013
Forlag	Association for Computing Machinery
Publikationsdato	2013
Sider	313-318
ISBN (Trykt)	978-1-4503-1758-0
DOI	https://doi.org/10.1145/2488222.2488281
Status	Udgivet - 2013

Dokumenter og links

10.1145/2488222.2488281

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Citationsformater

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Grand challenge: MapReduce-style processing of fast sensor data. / Madsen, Kasper Grud Skat; Su, Li; Zhou, Yongluan.
DEBS’13: Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems Arlington, TX, USA — June 29 - July 03, 2013. Association for Computing Machinery, 2013. s. 313-318.

Publikation: Kapitel i bog/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review

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AU - Su, Li

AU - Zhou, Yongluan

PY - 2013

Y1 - 2013

N2 - MapReduce is a popular scalable processing framework for large-scale data. In this paper, we first briefly present our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. We investigated how to utilize such a MapReduce-style platform for fast sensor data processing by taking the DEBS Grand Challenge 2013 as an example. Both the analysis and experiments verify that our approach can obtain highly scalable solutions.

AB - MapReduce is a popular scalable processing framework for large-scale data. In this paper, we first briefly present our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. We investigated how to utilize such a MapReduce-style platform for fast sensor data processing by taking the DEBS Grand Challenge 2013 as an example. Both the analysis and experiments verify that our approach can obtain highly scalable solutions.

KW - MapReduce

KW - Scalability

KW - Sensor data

KW - Sharing computation

U2 - 10.1145/2488222.2488281

DO - 10.1145/2488222.2488281

M3 - Article in proceedings

SN - 978-1-4503-1758-0

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EP - 318

BT - DEBS’13

PB - Association for Computing Machinery

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Grand challenge: MapReduce-style processing of fast sensor data

Abstract

Dokumenter og links

SDU link

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Citationsformater