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

7 Citations (Scopus)

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 MapRe-duce 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.

Original language	English
Title of host publication	DEBS’13 : Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems
Number of pages	6
Publisher	Association for Computing Machinery
Publication date	2013
Pages	313-318
ISBN (Electronic)	978-1-4503-1758-0
DOIs	https://doi.org/10.1145/2488222.2488281
Publication status	Published - 2013
Externally published	Yes
Event	7th ACM International Conference on Distributed Event-Based Systems - Arlington, United States Duration: 29 Jun 2013 → 3 Jul 2013 Conference number: 7

Conference

Conference	7th ACM International Conference on Distributed Event-Based Systems
Number	7
Country/Territory	United States
City	Arlington
Period	29/06/2013 → 03/07/2013

Access to Document

10.1145/2488222.2488281

Cite this

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. Association for Computing Machinery, 2013. p. 313-318.

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

Madsen, KGS, Su, L & Zhou, Y 2013, Grand challenge: MapReduce-style processing of fast sensor data. in DEBS’13: Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems. Association for Computing Machinery, pp. 313-318, 7th ACM International Conference on Distributed Event-Based Systems, Arlington, Texas, United States, 29/06/2013. https://doi.org/10.1145/2488222.2488281

@inproceedings{322aad98f5e140aa8439257dcd6435e0,

title = "Grand challenge: MapReduce-style processing of fast sensor data",

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 MapRe-duce 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.",

author = "Madsen, {Kasper Grud Skat} and Li Su and Yongluan Zhou",

year = "2013",

doi = "10.1145/2488222.2488281",

language = "English",

pages = "313--318",

booktitle = "DEBS{\textquoteright}13",

publisher = "Association for Computing Machinery",

note = "7th ACM International Conference on Distributed Event-Based Systems, DEBS 2013 ; Conference date: 29-06-2013 Through 03-07-2013",

}

TY - GEN

T1 - Grand challenge

T2 - 7th ACM International Conference on Distributed Event-Based Systems

AU - Madsen, Kasper Grud Skat

AU - Su, Li

AU - Zhou, Yongluan

N1 - Conference code: 7

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 MapRe-duce 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 MapRe-duce 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.

U2 - 10.1145/2488222.2488281

DO - 10.1145/2488222.2488281

M3 - Article in proceedings

SP - 313

EP - 318

BT - DEBS’13

PB - Association for Computing Machinery

Y2 - 29 June 2013 through 3 July 2013

ER -

Grand challenge: MapReduce-style processing of fast sensor data

Abstract

Conference

Access to Document

Fingerprint

Cite this