Abstract
Data-informed decision-making processes play a fundamental role across disciplines.To support these processes, knowledge needs to be extracted from high-dimensional(HD) and complex datasets. Visualizations play hereby a key role in identifying andunderstanding patterns within the data. However, the choice of visual mappingheavily influences the effectiveness of the visualization. While one design choice isuseful for a particular task, the very same design can make another analysis taskmore difficult, or even impossible. This doctoral thesis advances the quality andpattern-driven optimization of visualizations in two core areas by addressing theresearch question:“How can we effectively design visualizations to highlight patterns –using automatic and user-driven approaches?”The first part of the thesis deals with the question“how can we automaticallymeasure the quality of a particular design to optimize the layout?”We summarizethe state-of-the-art in quality-metrics research, describe the underlying concepts,optimization goals, constraints, and discuss the requirements of the algorithms.While numerous quality metrics exist for all major HD visualizations, researchlacks empirical studies to choose a particular technique for a given analysis task.In particular for parallel coordinates (PCP) and star glyphs, two frequently usedtechniques for high-dimensional data, no study exists which evaluates the impact ofdifferent axes orderings. Therefore, this thesis contributes an empirical study anda novel quality metric for both techniques. Based on our findings in the PCP study,we also contribute a formalization of how standard parallel coordinates distort theperception of patterns, in particular clusters. To minimize the effect, we propose anautomatic rendering technique.The second part of the thesis is user-centered and addresses the question“howcan analysts support the design of visualization to highlight particular patterns?”We contribute two techniques: Thev-plot designeris a chart authoring tool todesign custom hybrid charts for the comparative analysis of data distributions. Itautomatically recommends basic charts (e.g., box plots, violin-typed visualizations,and bar charts) and optimizes a custom hybrid chart called v-plot based on a setof analysis tasks.SMARTexploreuses a table metaphor and combines easy-to-applyinteraction with pattern-driven layouts of rows and columns and an automaticallycomputed reliability analysis based on statistical measures.In summary, this thesis contributes quality-metrics and user-driven approachesto advance the quality- and pattern-driven optimization of high-dimensional datavisualizations. The quality metrics and the grounding of the user-centered techniquesare derived from empirical user studies while the effectiveness of the implementedtools is shown by domain expert evaluations.
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