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» MapReduce: Simplified Data Processing on Large Clusters
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CLUSTER
2009
IEEE
14 years 2 months ago
Analyzing massive astrophysical datasets: Can Pig/Hadoop or a relational DBMS help?
Abstract— As the datasets used to fuel modern scientific discovery grow increasingly large, they become increasingly difficult to manage using conventional software. Parallel d...
Sarah Loebman, Dylan Nunley, YongChul Kwon, Bill H...
CIKM
2010
Springer
13 years 5 months ago
Adaptive query execution for data management in the cloud
A major component of many cloud services is query processing on data stored in the underlying cloud cluster. The traditional techniques for query processing on a cluster are those...
Adrian Daniel Popescu, Debabrata Dash, Verena Kant...
CLUSTER
2004
IEEE
13 years 11 months ago
Simplifying administration through dynamic reconfiguration. in a cooperative cluster storage system
Cluster Storage Systems where storage devices are distributed across a large number of nodes are able to reduce the I/O bottleneck problems present in most centralized storage sys...
Renaud Lachaize, Jorgen S. Hansen
CIKM
2009
Springer
14 years 2 months ago
Packing the most onto your cloud
Parallel dataflow programming frameworks such as Map-Reduce are increasingly being used for large scale data analysis on computing clouds. It is therefore becoming important to a...
Ashraf Aboulnaga, Ziyu Wang, Zi Ye Zhang
SC
2009
ACM
14 years 2 months ago
Lessons learned from a year's worth of benchmarks of large data clouds
In this paper, we discuss some of the lessons that we have learned working with the Hadoop and Sector/Sphere systems. Both of these systems are cloud-based systems designed to sup...
Yunhong Gu, Robert L. Grossman