The content of this thesis is bundled under the concept ofInteractive Visual Analysis, which makes use of the power of data visualization paired with interactive methods to steer al-gorithms and interpret results to generate knowledge. Figure 1.3 depicts the structure of this work, which is divided into two parts. The first part is about the identification and interpretation of multivariate patterns. I describe new methods to identify and interpret patterns in multivariate projections in Chapter 3. Also, I investigate whether users untrained in DR can interpret the depiction of a projection. In Chapter 4, I propose a new method to
1.3. Thesis Outline & Contributions
Chapter 3 Chapter 4 Chapter 5 Chapter 6
Visual Pattern Analysis &
Interpretation in Multivariate Subspaces
Visual Analysis of Temporal Multivariate Patterns
Topology-Preserving Off-screen Visualization
Effects of Mapping Strategy and Intrusion Adaption
Part I. Identification & Interpretation of Multivariate Patterns in Projections
Part II. Overview-Preservation in Large Projection Spaces Interactive Visual Analysis
Figure 1.3:Overview of the core chapters building the main contributions of this thesis. The chapters are assigned to two higher-level parts, each tackling one of the identified problems regarding projections for visual analysis of multivariate data. The interactive visual analysis is a key concept dominating this thesis, in particular Chapters 3 and 4, where the interpretation of multivariate projections is regarded. Chapter 5 presents a highly interactive approach. However, the focus is on preserving overview in 2D projections and only partially relates to interactive analysis. Chapter 6 analyzes the effects of the overview-preservation introduced in Chapter 5.
generate patterns using sequential projections applied to data sequences. The second part deals with the navigation and context-preservation of the space, spanned by the projection, using off-screen visualization. Chapter 5 introduces the concept of off-screen visualization and contributes methods to handle large datasets. A core design decision to preserve the dimensions of the navigated space is to use an adaptive border intrusion. This design decision was evaluated together with the projection strategy, and the results are described in Chapter 6.
This dissertation claims the following two keycontributions:
• The development and evaluation of novel interactive visual analysis methods to foster identification and interpretation of patterns in multivariate data spaces using projections.
• The development and investigation of off-screen visualization techniques and strategies for context-aware navigation of information spaces spanned by the projection.
These contributions distribute among the chapters as follows:
Chapter 3: This Chapter makes the following contributions towards the detection and interpretation of patterns in multivariate projections. First, a visual analytics system that integrates mixed data types into the projection. Since real-world datasets typically comprise different data types beyond numbers and categories, this system enables analysts to explore their domain-specific data. However, domain experts have diverse backgrounds and may not be used to such representations. To answer the question about interpretability, I then conducted a user study to investigate whether domain experts untrained in advanced statistics can interpret the results of a multivariate projection. The results show that they can do so,
given tasks that are particularly relevant in their domain. I observed that domain experts included attributes differently into the projection to verify their hypotheses in different subspaces. To tackle the question of how patterns change across different subspaces, I further contribute a method that includes subspaces into a small-multiple environment and enables users to inspect the pattern transitions among subspaces. Related to that, I developed a new similarity measure between multivariate projections to order the small multiples.
Chapter 4: In this Chapter, I contribute a visual method named Temporal Multidimensional Scaling (TMDS) that creates projections to identify patterns in multivariate data that may in-clude sequential dependencies. A sliding window is applied to the data and a one-dimensional projection computed for each window. Aligning the projections one after another reveals not only patterns based on similarity but also patterns where sequences play a key role and contribute to the understanding. Based on the sequential projections, I furthermore contribute a method to find similar patterns in the resulting projection space based on a previously known pattern.
Chapter 5: This Chapter opens the design space of off-screen visualizations for context-preservation and contributes and discusses three interactive techniques that aim at different data characteristics. First, I propose an off-screen visualization, introducing a data-driven border region. Based on rasterization, points and shapes plus an additional data encoding can be preserved while navigating spatial datasets. Second and based on the rasterization, I propose to encode a second data value which I showcase using uncertainty information about the data. Third, I go one step further and propose to use multivariate star glyphs to encode more than two dimensions for off-screen information. All three approaches are based on aggregation. Since the aggregation using a dedicated border region represents the logical consequence compared to state-of-the-art techniques, I evaluated the usage against the latest off-screen technique making use of aggregation, namely HaloDot (instead of a border, HaloDot shows aggregated off-screen information using halos intersecting the viewport).
Results are in favor of using a border region.
Chapter 6: In Chapter 5, I focus on visualizing off-screen objects. However, there are two unanswered questions: firstly, how can the dimensions of the navigated space be reflected and, secondly, which projection strategy (orthographic or radial) meets the users’ intuition?
Here, I introduce an adaptive border intrusion which I evaluated together with the projection strategy. There are two strategies: The orthographic strategy divides the off-screen space into eight different areas and projects the objects perpendicular to the viewport. In contrast, the radial strategy projects the off-screen located objects along a line towards the center of the viewport. The results show that there is no disadvantage in reflecting the dimensions of the navigated space in an additional encoding. Also, users perform significantly more accurate using the orthographic projection.