Integrated Machine Learning in the Kepler Scientific Workflow System | Zendy

Mai H. Nguyen | Zendy; Daniel Crawl | Zendy; Tahereh Masoumi | Zendy; İlkay Altıntaş | Zendy

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Integrated Machine Learning in the Kepler Scientific Workflow System

Author(s) -

Mai H. Nguyen,

Daniel Crawl,

Tahereh Masoumi,

İlkay Altıntaş

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.05.545

Subject(s) - computer science , workflow , implementation , scalability , kepler , workflow technology , cluster analysis , process (computing) , workflow engine , machine learning , feature (linguistics) , artificial intelligence , software engineering , database , programming language , stars , linguistics , philosophy , computer vision

We present a method to integrate multiple implementations of a machine learning algorithm in Kepler actors. This feature enables the user to compare accuracy and scalability of various implementations of a machine learning technique without having to change the workflow. These actors are based on the Execution Choice actor. They can be incorporated into any workflow to provide machine learning functionality. We describe a use case where actors that provide several implementations of k-means clustering can be used in a workflow to process sensor data from weather stations for predicting wildfire risks

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