A Community-Driven Intelligent Scientific Workflow Recommender System

As current satellite measurements rapidly magnify the accumulation of more than 40 years of scientific knowledge, new discoveries increasingly require collaborative integration and adaptation of various data-driven software components (tools). In recent years, scientists have learned how to codify tools into reusable software modules that can be chained into multi-step executable workflows. However, although computing technologies continue to improve, adoption via the sharing and reuse of modules and workflows remains a big challenge. This project tackles this challenge from a novel angle, to study how to leverage peer scientists’ best practice to help facilitate the discovery and reuse of Earth science modules developed by others. Machine Learning/Deep Learning-powered service classification, semantic discovery, recommendation, automatic composition, deployment and scheduling over the cloud are our research focuses.

This project is sponored by National Aeronautics and Space Administration.