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Skew-resistant parallel processing of feature-extracting scientific user-defined functions
2010
Proceedings of the 1st ACM symposium on Cloud computing - SoCC '10
Scientists today have the ability to generate data at an unprecedented scale and rate and, as a result, they must increasingly turn to parallel data processing engines to perform their analyses. However, the simple execution model of these engines can make it difficult to implement efficient algorithms for scientific analytics. In particular, many scientific analytics require the extraction of features from data represented as either a multidimensional array or points in a multidimensional
doi:10.1145/1807128.1807140
dblp:conf/cloud/KwonBHR10
fatcat:uycukwm6ebdlhffywcv7ypzdjy