Towards optimizing the execution of spark scientific workflows using machine learning-based parameter tuning.
Douglas E. M. de OliveiraFábio PortoCristina BoeresDaniel de OliveiraPublished in: Concurr. Comput. Pract. Exp. (2021)
Keyphrases
- parameter tuning
- scientific workflows
- machine learning
- ink bleed
- scientific data
- parameter settings
- service oriented
- web services
- semantic annotation
- workflow systems
- data analysis
- workflow execution
- text mining
- information extraction
- data collection
- model selection
- natural language processing
- knowledge acquisition
- artificial intelligence
- data mining
- business processes
- data model