Bruhwiler, Kevin, authorPallickara, Shrideep, advisorPallickara, Sangmi Lee, advisorGhosh, Sudipto, committee memberChandrasekaran, Venkatachalam, committee member2021-01-112021-01-112020https://hdl.handle.net/10217/219537The growth in observational data volumes over the past decade has occurred alongside a need to make sense of the phenomena that underpin them. Visualization is a key component of the data wrangling process that precedes the analyses that informs these insights. The crux of this study is interactive visualizations of spatiotemporal phenomena from voluminous datasets. Spatiotemporal visualizations of voluminous datasets introduce challenges relating to interactivity, overlaying multiple datasets and dynamic feature selection, resource capacity constraints, and scaling. Our methodology to address these challenges relies on a novel mix of algorithms and systems innovations working in concert to ensure effective apportioning and amortization of workloads and enables interactivity during visualizations. In particular our research prototype, Aperture, leverages sketching algorithms, effective query predicate generation and evaluation, avoids performance hotspots, harnesses coprocessors for hardware acceleration, and convolutional neural network based encoders to render visualizations while preserving responsiveness and interactivity. Finally, we also explore issues in effective containerization to support visualization workloads. We also report on several empirical benchmarks that profile and demonstrate the suitability of our methodology to preserve interactivity while utilizing resources effectively to scale.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text