Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering

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dc.contributor.authorEndert, Alexen
dc.contributor.committeechairNorth, Christopher L.en
dc.contributor.committeememberBowman, Douglas A.en
dc.contributor.committeememberLeman, Scotland C.en
dc.contributor.committeememberMay, Richarden
dc.contributor.committeememberQuek, Francis K. H.en
dc.contributor.departmentComputer Science and Applicationsen
dc.date.accessioned2014-03-14T20:13:54Zen
dc.date.adate2012-07-18en
dc.date.available2014-03-14T20:13:54Zen
dc.date.issued2012-07-10en
dc.date.rdate2012-07-18en
dc.date.sdate2012-07-11en
dc.description.abstractUser interaction in visual analytic systems is critical to enabling visual data exploration. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. For example, two-dimensional layouts of high-dimensional data can be generated by dimension reduction models, and provide users with an overview of the relationships between information. However, exploring such spatializations can require expertise with the internal mechanisms and parameters of these models. The core contribution of this work is semantic interaction, capable of steering such models without requiring expertise in dimension reduction models, but instead leveraging the domain expertise of the user. Semantic interaction infers the analytical reasoning of the user with model updates, steering the dimension reduction model for visual data exploration. As such, it is an approach to user interaction that leverages interactions designed for synthesis, and couples them with the underlying mathematical model to provide computational support for foraging. As a result, semantic interaction performs incremental model learning to enable synergy between the user's insights and the mathematical model. The contributions of this work are organized by providing a description of the principles of semantic interaction, providing design guidelines through the development of a visual analytic prototype, ForceSPIRE, and the evaluation of the impact of semantic interaction on the analytic process. The positive results of semantic interaction open a fundamentally new design space for designing user interactions in visual analytic systems. This research was funded in part by the National Science Foundation, CCF-0937071 and CCF-0937133, the Institute for Critical Technology and Applied Science at Virginia Tech, and the National Geospatial-Intelligence Agency contract #HMI1582-05-1-2001.en
dc.description.degreePh. D.en
dc.identifier.otheretd-07112012-123927en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-07112012-123927/en
dc.identifier.urihttp://hdl.handle.net/10919/28265en
dc.publisherVirginia Techen
dc.relation.haspartEndert_A_D_2012.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectuser interactionen
dc.subjectvisual analyticsen
dc.subjectmodel steeringen
dc.subjectvisualizationen
dc.titleSemantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steeringen
dc.typeDissertationen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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