Archimedes: A Framework to Support Distributional Similarity Analysis over Arbitrary Spatiotemporal Scopes at Scale

As data volumes have grown, they offer opportunities to extract insights from them. The class of datasets we consider are spatiotemporal i.e., data that have spatial and temporal dimensions associated with them. In this study, we describe our methodology to support distributional similarity analysis at scale. Such distributional analyses are critical in identifying overall trends in spatiotemporally evolving phenomena, and can be used to inform model calibration and decision making. Our methodology leverages a mix of statistical, algorithmic, and systems approaches to facilitate effective analyses over voluminous datasets. We profile several aspects of our methodology, and our benchmarks validate several of our design choices.