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Scalable visual analytics over voluminous spatiotemporal data

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dc.contributor.authorStern, Ryan, author
dc.contributor.authorPallickara, Shrideep, advisor
dc.contributor.authorPallickara, Sangmi, committee member
dc.contributor.authorBohm, A. P. Wim, committee member
dc.contributor.authorBreidt, F. Jay, committee member
dc.date.accessioned2019-01-07T17:19:17Z
dc.date.available2019-01-07T17:19:17Z
dc.date.issued2018
dc.description.abstractVisualization is a critical part of modern data analytics. This is especially true of interactive and exploratory visual analytics, which encourages speedy discovery of trends, patterns, and connections in data by allowing analysts to rapidly change what data is displayed and how it is displayed. Unfortunately, the explosion of data production in recent years has led to problems of scale as storage, processing, querying, and visualization have struggled to keep pace with data volumes. Visualization of spatiotemporal data pose unique challenges, thanks in part to high-dimensionality in the input feature space, interactions between features, and the production of voluminous, high-resolution outputs. In this dissertation, we address challenges associated with supporting interactive, exploratory visualization of voluminous spatiotemporal datasets and underlying phenomena. This requires the visualization of millions of entities and changes to these entities as the spatiotemporal phenomena unfolds. The rendering and propagation of spatiotemporal phenomena must be both accurate and timely. Key contributions of this dissertation include: 1) the temporal and spatial coupling of spatially localized models to enable the visualization of phenomena at far greater geospatial scales; 2) the ability to directly compare and contrast diverging spatiotemporal outcomes that arise from multiple exploratory "what-if" queries; and 3) the computational framework required to support an interactive user experience in a heavily resource-constrained environment. We additionally provide support for collaborative and competitive exploration with multiple synchronized clients.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierStern_colostate_0053A_15155.pdf
dc.identifier.urihttps://hdl.handle.net/10217/193130
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright 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.
dc.subjectmachine learning
dc.subjectspatiotemporal
dc.subjectdistributed systems
dc.subjectvisual analytics
dc.subjectnational-scale
dc.titleScalable visual analytics over voluminous spatiotemporal data
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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