Modern scientific workflows are data-driven and are often executed on heterogeneous, high-performance computing infrastructures . X-FLASH incorporates novel hyperparameter tuning and data mining approaches for improving the performance of the machine learning algorithms to classify the anomalous TCP packets . The tool outperformed the existing approach up to 28\%, 29\%, and 40\% relatively for F-measure, G-score, and recall in less than30 evaluations . The researchers recommendfuture research to have additional tuning study as a new standard, at least inthe area of scientific workflow anomaly detection. We recommend future research to . have additional . research into the area of . scientific . workflows as well as the . area of ‘anomalies and failures’ in the workflows’. For more information, please contact the authors of this article, visit http://www.smithsmith.smith.org/smith

Author(s) : Huy Tu, George Papadimitriou, Mariam Kiran, Cong Wang, Anirban Mandal, Ewa Deelman, Tim Menzies

Links : PDF - Abstract

Code :

https://github.com/dsmic/LearnMultiplyByHand


Coursera

Keywords : scientific - workflows - area - data - research -

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