SRC15. Touring Dataland? Automated Recommendations for the Big Data Traveler
Student: William C. Agnew (University of Chicago)
Supervisor: Kyle Chard (University of Chicago)
Abstract: We explore how recommendation techniques can be adapted and applied to big data science. Using features specific to big data science, we develop a set of data location prediction heuristics. 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.
Two-page extended abstract: pdf