Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning

Research article (Concurrency and Computation Practice and Experience, 2020) · cited 13× · AI/ML
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Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning. Retrieved May 24, 2026, from https://4ort.xyz/entity/towards-optimizing-the-execution-of-spark-scientific-workflows-using-machine-learningbased-parameter-tuning
MLA “Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/towards-optimizing-the-execution-of-spark-scientific-workflows-using-machine-learningbased-parameter-tuning.
BibTeX @misc{4ortxyz_towards-optimizing-the-execution-of-spark-scientific-workflows-using-machine-learningbased-parameter-tuning_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning}}, year = {2026}, url = {https://4ort.xyz/entity/towards-optimizing-the-execution-of-spark-scientific-workflows-using-machine-learningbased-parameter-tuning}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning — https://4ort.xyz/entity/towards-optimizing-the-execution-of-spark-scientific-workflows-using-machine-learningbased-parameter-tuning (retrieved 2026-05-24)

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