Grand challenge: MapReduce-style processing of fast sensor data

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 -