Main Article Content
Rising storage and computational capacities have led to the accumulation of voluminous datasets. These datasets contain insights that describe natural phenomena, usage patterns, trends, and other aspects of complex, real-world systems. Statistical and machine learning models are often employed to identify these patterns or attributes of interest. However, a wide array of potentially relevant models and parameterizations exist, and may provide the best performance only after preprocessing steps have been carried out. Our distributed analytics platform, TRIDENT, facilitates the modeling process by providing high-level data exploration functionality as well as guidance for creation of effective models. TRIDENT handles  data partitioning and storage,  metadata extraction and indexing, and  selective retrievals or transformations to prepare and generate training data. In this study, we evaluate TRIDENT in the context of a 1.1 peat byte epidemiology dataset generated by a disease spread simulation; such datasets are often used in planning for national-scale outbreaks in animal populations.