C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
COMP Superscalar: Bringing GRID Superscalar and GCM Together
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Bridging the Gap between Desktop and the Cloud for eScience Applications
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Twister: a runtime for iterative MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
A Cloud Framework for Parameter Sweeping Data Mining Applications
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Enabling e-science applications on the cloud with COMPSs
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
Scalable script-based data analysis workflows on clouds
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
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The advent of Cloud computing has given to researchers the ability to access resources that satisfy their growing needs, which could not be satisfied by traditional computing resources such as PCs and locally managed clusters. On the other side, such ability, has opened new challenges for the execution of their computational work and the managing of massive amounts of data into resources provided by different private and public infrastructures. COMP Superscalar (COMPSs) is a programming framework that provides a programming model and a runtime that ease the development of applications for distributed environments and their execution on a wide range of computational infrastructures. COMPSs has been recently extended in order to be interoperable with several cloud technologies like Amazon, OpenNebula, Emotive and other OCCI compliant offerings. This paper presents the extensions of this interoperability layer to support the execution of COMPSs applications into the Windows Azure Platform. The framework has been evaluated through the porting of a data mining workflow to COMPSs and the execution on an hybrid testbed.