An Ensemble-Based Recommendation Engine for Scientific Data Transfers

Abstract

Big data scientists face the challenge of locating valuable datasets across a network of distributed storage locations. We explore methods for recommending storage locations (“endpoints”) for users based on a range of prediction models including collaborative filtering and heuristics that consider available information such as user, institution, access history, endpoint ownership, and endpoint usage. We combine the strengths of these models by training a deep recurrent neural network on their predictions. Collectively we show, via analysis of historical usage from the Globus research data management service, that our approach can predict the next storage location accessed by users with 80.3% and 95.3% accuracy for top-1 and top-3 recommendations, respectively. Additionally, our heuristics can predict the endpoints that users will use in the future with over 75% precision and recall.