Designing and Evaluating Object-Level Interaction to Support Human-Model Communication in Data Analysis

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dc.contributor.authorSelf, Jessica Zeitzen
dc.contributor.committeechairNorth, Christopher L.en
dc.contributor.committeememberPerez-Quinonez, Manuel A.en
dc.contributor.committeememberChang, Remco K.en
dc.contributor.committeememberHouse, Leanna L.en
dc.contributor.committeememberLuther, Kurten
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2016-05-10T08:00:31Zen
dc.date.available2016-05-10T08:00:31Zen
dc.date.issued2016-05-09en
dc.description.abstractHigh-dimensional data appear in all domains and it is challenging to explore. As the number of dimensions in datasets increases, the harder it becomes to discover patterns and develop insights. Data analysis and exploration is an important skill given the amount of data collection in every field of work. However, learning this skill without an understanding of high-dimensional data is challenging. Users naturally tend to characterize data in simplistic one-dimensional terms using metrics such as mean, median, mode. Real-world data is more complex. To gain the most insight from data, users need to recognize and create high-dimensional arguments. Data exploration methods can encourage thinking beyond traditional one-dimensional insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be manipulated to assess the data from multiple perspectives. Manipulating can be difficult for users without a strong knowledge of the underlying algorithms. Visual analytics tools that afford object-level interaction (OLI) allow for generation of more complex insights, despite inexperience with multivariate data or the underlying algorithm. The goal of this research is to develop and test variations on types of interactions for interactive visual analytic systems that enable users to tweak model parameters directly or indirectly so that they may explore high-dimensional data. To study interactive data analysis, we present an interface, Andromeda, that enables non-experts of statistical models to explore domain-specific, high-dimensional data. This application implements interactive weighted multidimensional scaling (WMDS) and allows for both parametric and observation-level interaction to provide in-depth data exploration. We performed multiple user studies to answer how parametric and object-level interaction aid in data analysis. With each study, we found usability issues and then designed solutions for the next study. With each critique we uncovered design principles of effective, interactive, visual analytic tools. The final part of this research presents these principles supported by the results of our multiple informal and formal usability studies. The established design principles focus on human-centered usability for developing interactive visual analytic systems that enable users to analyze high-dimensional data through object-level interaction.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:7586en
dc.identifier.urihttp://hdl.handle.net/10919/70950en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectvisual analyticsen
dc.subjecthuman-computer interactionen
dc.subjectinterface designen
dc.subjectdimension reductionen
dc.titleDesigning and Evaluating Object-Level Interaction to Support Human-Model Communication in Data Analysisen
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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