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From performance profiling to predictive analytics while evaluating hadoop cost-efficiency in ALOJA.

, , , , , , , , and . IEEE BigData, page 1220-1229. IEEE Computer Society, (2015)

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Science in the cloud: lessons from three years of research projects on microsoft azure., , , , and . ScienceCloud@HPDC, page 1-8. ACM, (2014)ALOJA-ML: A Framework for Automating Characterization and Knowledge Discovery in Hadoop Deployments., , , , , and . KDD, page 1701-1710. ACM, (2015)Parallelization of the GROMOS87 Molecular Dynamics Code: An Update., and . HPCN Europe, volume 1067 of Lecture Notes in Computer Science, page 170-176. Springer, (1996)Part 3 - Introduction.. The Fourth Paradigm, Microsoft Research, (2009)ALOJA: A systematic study of Hadoop deployment variables to enable automated characterization of cost-effectiveness., , , , , , , , , and 3 other author(s). IEEE BigData, page 905-913. IEEE Computer Society, (2014)ALOJA: A Framework for Benchmarking and Predictive Analytics in Hadoop Deployments., , , , , and . IEEE Trans. Emerg. Top. Comput., 5 (4): 480-493 (2017)Value Propositions for Information Systems in Healthcare., and . HICSS, page 257. IEEE Computer Society, (2008)ALOJA: A Framework for Benchmarking and Predictive Analytics in Big Data Deployments., , , , , and . CoRR, (2015)Applying Microsoft Research Technologies to the 4th Paradigm in Scientific Research.. GPC, volume 6646 of Lecture Notes in Computer Science, page 1-2. Springer, (2011)Parallelisation of the molecular dynamics code GROMOS87 for distributed memory parallel architectures., , and . HPCN Europe, volume 919 of Lecture Notes in Computer Science, page 875-879. Springer, (1995)