2021/10/26 by Azita Nouri, Nouri, Azita, Philip E. Davis +5
Business, Management and Accounting · Computer Science · Decision Sciences · #Big Data and Business Intelligence #Computer science #Context (archaeology) #Data Stream Mining Techniques #Data science #Database #Distributed #FOS: Computer and information sciences #Parallel #Research Data Management Practices #Scientific Computing and Data Management #Software engineering #Workflow #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.2110.13999
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/10/26 · openalex publication_date 2021/10/26 · arxiv updated 2021/10/28 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/05
In this survey, we discuss the challenges of executing scientific workflows\nas well as existing Machine Learning (ML) techniques to alleviate those\nchallenges. We provide the context and motivation for applying ML to each step\nof the execution of these workflows. Furthermore, we provide recommendations on\nhow to extend ML techniques to unresolved challenges in the execution of\nscientific workflows. Moreover, we discuss the possibility of using ML\ntechniques for in-situ operations. We explore the challenges of in-situ\nworkflows and provide suggestions for improving the performance of their\nexecution using ML techniques.\n