| Summary: | Annotation Applications and experiments in all areas of science are becoming increasingly complex and more demanding in terms of their computational and data requirements Some applications generate data volumes reaching hundreds of terabytes and even petabytes Analyzing, visualizing, and disseminating these large data sets has become a major challenge and data intensive computing is now considered as the fourth paradigm in scientific discovery after theoretical, experimental, and computational science As scientific applications become more data intensive, the technologies of handling Big Data have gathered great importance This necessity has made that applications have seen an increasing adoption on clouds infrastructures The computing models, system software, programming models, analysis frameworks, and other clouds services need to evolve and accommodate them to face the challenge of big data applications.
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