Demo: elastic mapreduce-style processing of fast data

Kasper Grud Skat Madsen; Yongluan Zhou

doi:10.1145/2488222.2488350

Demo: elastic mapreduce-style processing of fast data

Kasper Grud Skat Madsen, Yongluan Zhou

3 Citations (Scopus)

Abstract

MapReduce is a popular scalable processing framework for large-scale data. In this paper we demonstrate Enorm, which represents our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. Most existing work have focused on how to extend the MapReduce framework for low-latency data stream processing, but overlooked the problem of obtaining runtime elasticity. The demonstration focuses on two important features in Enorm. (1) sharing aggregate computations among overlapping windows and (2) runtime elasticity.

Original language	English
Title of host publication	DEBS’13 : Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems Arlington, TX, USA — June 29 - July 03, 2013
Number of pages	2
Publisher	Association for Computing Machinery
Publication date	2013
Pages	335-336
ISBN (Print)	978-1-4503-1758-0
DOIs	https://doi.org/10.1145/2488222.2488350
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

10.1145/2488222.2488350

Cite this

Demo: elastic mapreduce-style processing of fast data. / Madsen, Kasper Grud Skat; 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. p. 335-336.

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

Madsen, KGS & Zhou, Y 2013, Demo: elastic mapreduce-style processing of fast data. in 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, pp. 335-336, 7th ACM International Conference on Distributed Event-Based Systems, Arlington, Texas, United States, 29/06/2013. https://doi.org/10.1145/2488222.2488350

@inproceedings{78200ec97a234f2cb4eaaf92dcf5eb3d,

title = "Demo: elastic mapreduce-style processing of fast data",

abstract = "MapReduce is a popular scalable processing framework for large-scale data. In this paper we demonstrate Enorm, which represents our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. Most existing work have focused on how to extend the MapReduce framework for low-latency data stream processing, but overlooked the problem of obtaining runtime elasticity. The demonstration focuses on two important features in Enorm. (1) sharing aggregate computations among overlapping windows and (2) runtime elasticity.",

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

year = "2013",

doi = "10.1145/2488222.2488350",

language = "English",

isbn = "978-1-4503-1758-0",

pages = "335--336",

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 - Demo: elastic mapreduce-style processing of fast data

AU - Madsen, Kasper Grud Skat

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 demonstrate Enorm, which represents our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. Most existing work have focused on how to extend the MapReduce framework for low-latency data stream processing, but overlooked the problem of obtaining runtime elasticity. The demonstration focuses on two important features in Enorm. (1) sharing aggregate computations among overlapping windows and (2) runtime elasticity.

AB - MapReduce is a popular scalable processing framework for large-scale data. In this paper we demonstrate Enorm, which represents our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. Most existing work have focused on how to extend the MapReduce framework for low-latency data stream processing, but overlooked the problem of obtaining runtime elasticity. The demonstration focuses on two important features in Enorm. (1) sharing aggregate computations among overlapping windows and (2) runtime elasticity.

U2 - 10.1145/2488222.2488350

DO - 10.1145/2488222.2488350

M3 - Article in proceedings

SN - 978-1-4503-1758-0

SP - 335

EP - 336

BT - DEBS’13

PB - Association for Computing Machinery

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

Y2 - 29 June 2013 through 3 July 2013

ER -

Demo: elastic mapreduce-style processing of fast data

Abstract

Conference

Access to Document

Fingerprint

Cite this