Touring Dataland?Automated Recommendations for the Big Data Traveler

We explore how recommendation techniques can be adapted and applied to big data science to predict data storage locations to users. Specifically, we present a collection of heuristics that use features specific to big data science. We combine these heuristics into a single recommendation engine using a deep recurrent neural network. We show, via analysis of historical Globus operations, that our approaches can predict the storage locations accessed by users with 78.2% and 95.5% accuracy for top-1 and top-3 recommendations, respectively.