Date of Award

7-2018

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Engineering and Sciences

First Advisor

Thomas Eskridge

Second Advisor

William Allen

Third Advisor

Carlos Otero

Fourth Advisor

Philip Bernhard

Abstract

This thesis describes a development environment that integrates big data architectures and deep learning models to facilitate rapid experimentation. The thesis makes three major contributions: First, it describes a big-data architecture that supports big data collection and organization supporting deep learning models. Second, it describes a language used to create a data view that converts the various big data streams into a view that can be used by a deep learning system. Third, it demonstrates the system’s effectiveness by applying the tool to several different deep learning applications.

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