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dc.contributor.authorReach, Andrew McCaleben_US
dc.date.accessioned2017-09-09T08:00:35Z
dc.date.available2017-09-09T08:00:35Z
dc.date.issued2017-09-08
dc.identifier.othervt_gsexam:12621en_US
dc.identifier.urihttp://hdl.handle.net/10919/78848
dc.description.abstractInformation visualization is a powerful tool for understanding large datasets. However, many commonly-used techniques in information visualization are not C^1 smooth, i.e. when represented as a function, they are either discontinuous or have a discontinuous first derivative. For example, histograms are a non-smooth visualization of density. Not only are histograms non-smooth visually, but they are also non-smooth over their parameter space, as they change abruptly in response to smooth change of bin width or bin offset. For large data visualization, histograms are commonly used in place of smooth alternatives, such as kernel density plots, because histograms can be constructed from data cubes, allowing histograms to be constructed quickly for large datasets. Another example of a non-smooth technique in information visualization is the commonly-used transition approach to animation. Although transitions are designed to create smooth animations, the transition technique produces animations that have velocity discontinuities if the target is changed before the transition has finished. The smooth and efficient zooming and panning technique also shares this problem---the animations produced are smooth while in-flight, but they have velocity discontinuities at the beginning and end and of the animation as well as velocity discontinuities when interrupted. This dissertation applies ideas from signal processing to construct smooth alternatives to these non-smooth techniques. To visualize density for large datasets, we propose BLOCs, a smooth alternative to data cubes that allows kernel density plots to be constructed quickly for large datasets after an initial preprocessing step. To create animations that are smooth even when interrupted, we present LTI animation, a technique that uses LTI filters to create animations that are smooth, even when interrupted. To create zooming and panning animations that are smooth, even when interrupted, we generalize signal processing systems to Riemannian manifolds, resulting in smooth, efficient, and interruptible animations.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectInformation visualizationen_US
dc.subjectsignal processingen_US
dc.subjectRiemannian geometryen_US
dc.titleSmooth Interactive Visualizationen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePHDen_US
thesis.degree.namePHDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairNorth, Christopher Len_US
dc.contributor.committeememberPolys, Nicholas Fearingen_US
dc.contributor.committeememberBaumann, William Ten_US
dc.contributor.committeememberGracanin, Denisen_US
dc.contributor.committeememberStasko, John Ten_US


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