Information uncertainty is inherent in many real-world problems and adds a layer of complexity to modeling and visualization tasks. This often causes users to ignore uncertainty, especially when it comes to visualization, thereby discarding valuable knowledge. A coherent framework for the modeling and visualization of information uncertainty is needed to address this issue In this work, we have identified four major barriers to the uptake of uncertainty modeling and visualization. Firstly, there are numerous uncertainty modeling tech- niques and users are required to anticipate their uncertainty needs before building their data model. Secondly, parameters of uncertainty tend to be treated at the same level as variables making it easy to introduce avoidable errors. This causes the uncertainty technique to dictate the structure of the data model. Thirdly, propagation of uncertainty information must be manually managed. This requires user expertise, is error prone, and can be tedious. Finally, uncertainty visualization techniques tend to be developed for particular uncertainty types, making them largely incompatible with other forms of uncertainty information. This narrows the choice of visualization techniques and results in a tendency for ad hoc uncertainty visualization. The aim of this thesis is to present an integrated information uncertainty modeling and visualization environment that has the following main features: information and its uncertainty are encapsulated into atomic variables, the propagation of uncertainty is automated, and visual mappings are abstracted from the uncertainty information data type. Spreadsheets have previously been shown to be well suited as an approach to visu- alization. In this thesis, we devise a new paradigm extending the traditional spreadsheet to intrinsically support information uncertainty.Our approach is to design a framework that integrates uncertainty modeling tech- niques into a hierarchical order based on levels of detail. The uncertainty information is encapsulated and treated as a unit allowing users to think of their data model in terms of the variables instead of the uncertainty details. The system is intrinsically aware of the encapsulated uncertainty and is therefore able to automatically select appropriate uncertainty propagation methods. A user-objectives based approach to uncertainty visualization is developed to guide the visual mapping of abstracted uncertainty information. Two main abstractions of uncertainty information are explored for the purpose of visual mapping: the Unified Uncertainty Model and the Dual Uncertainty Model. The Unified Uncertainty Model provides a single view of uncertainty for visual mapping, whereas the Dual Uncertainty Model distinguishes between possibilistic and probabilistic views. Such abstractions provide a buffer between the visual mappings and the uncertainty type of the underly- ing data, enabling the user to change the uncertainty detail without causing the visual- ization to fail. Two main case studies are presented. The first case study covers exploratory and forecasting tasks in a business planning context. The second case study inves- tigates sensitivity analysis for financial decision support. Two minor case studies are also included: one to investigate the relevancy visualization objective applied to busi- ness process specifications, and the second to explore the extensibility of the system through General Purpose Graphics Processor Unit (GPGPU) use. A quantitative anal- ysis compares our approach to traditional analytical and numerical spreadsheet-based approaches. Two surveys were conducted to gain feedback on the from potential users. The significance of this work is that we reduce barriers to uncertainty modeling and visualization in three ways. Users do not need a mathematical understanding of the uncertainty modeling technique to use it; uncertainty information is easily added, changed, or removed at any stage of the process; and uncertainty visualizations can be built independently of the uncertainty modeling technique.

Uncertainty is inherent in many real-world settings; for example, in a combat situation, darkness may prevent a soldier from classifying approaching troops as friendly or hostile. In an environment plagued with uncertainty, decision-support systems, such as sensor-based networks, may make faulty assumptions about field conditions, especially when information is incomplete, or sensor operations are disrupted. Displaying the factors that contribute to uncertainty informs the decision-making process for a human operator, but at the expense of limited cognitive resources, such as attention, memory, and workload. This research applied principles of perceptual cognition to human-computer interface design to introduce uncertainty visualizations in an adaptive approach that improved the operator's decision-making process, without unduly burdening the operator's cognitive load. An adaptive approach to uncertainty visualization considers the cognitive burden of all visualizations, and reduces the visualizations according to relevancy as the user's cognitive load increases. Experiments were performed using 24 volunteer participants using a simulated environment that featured both intrinsic load, and characteristics of uncertainty. The experiments conclusively demonstrated that adaptive uncertainty visualization reduced the cognitive burden on the operator's attention, memory, and workload, resulting in increased accuracy rates, faster response times, and a higher degree of user satisfaction. This research adds to the body of knowledge regarding the use of uncertainty visualization in the context of cognitive load. Existing research has not identified techniques to support uncertainty visualization, without further burdening cognitive load. This research identified principles, such as goal-oriented visualization, and salience, which promote the use of uncertainty visualization for improved decision-making without increasing cognitive load. This research has extensive significance in fields where both uncertainty and cognitive load factors can reduce the effectiveness of decision-makers, such as sensor-based systems used in the military, or in first-responder situations.

The most important resources to fulfill today’s energy demands are fossil fuels, such as oil and natural gas. When exploiting hydrocarbon reservoirs, a detailed and credible model of the subsurface structures to plan the path of the borehole, is crucial in order to minimize economic and ecological risks. Before that, the placement, as well as the operations of oil rigs need to be planned carefully, as off-shore oil exploration is vulnerable to hazards caused by strong currents. The oil and gas industry therefore relies on accurate ocean forecasting systems for planning their operations. This thesis presents visual workflows for creating subsurface models as well as planning the placement and operations of off-shore structures. Creating a credible subsurface model poses two major challenges: First, the structures in highly ambiguous seismic data are interpreted in the time domain. Second, a velocity model has to be built from this interpretation to match the model to depth measurements from wells. If it is not possible to obtain a match at all positions, the interpretation has to be updated, going back to the first step. This results in a lengthy back and forth between the different steps, or in an unphysical velocity model in many cases. We present a novel, integrated approach to interactively creating subsurface models from reflection seismics, by integrating the interpretation of the seismic data using an interactive horizon extraction technique based on piecewise global optimization with velocity modeling. Computing and visualizing the effects of changes to the interpretation and velocity model on the depth-converted model, on the fly enables an integrated feedback loop that enables a completely new connection of the seismic data in time domain, and well data in depth domain. For planning the operations of off-shore structures we present a novel integrated visualization system that enables interactive visual analysis of ensemble simulations used in ocean forecasting, i.e, simulations of sea surface elevation. Changes in sea surface elevation are a good indicator for the movement of loop current eddies. Our visualization approach enables their interactive exploration and analysis. We enable analysis of the spatial domain, for planning the placement of structures, as well as detailed exploration of the temporal evolution at any chosen position, for the prediction of critical ocean states that require the shutdown of rig operations. We illustrate this using a real-world simulation of the Gulf of Mexico.

abstract: The proper quantification and visualization of uncertainty requires a high level of domain knowledge. Despite this, few studies have collected and compared the roles, experiences and opinions of scientists in different types of uncertainty analysis. I address this gap by conducting two types of studies: 1) a domain characterization study with general questions for experts from various fields based on a recent literature review in ensemble analysis and visualization, and; 2) a long-term interview with domain experts focusing on specific problems and challenges in uncertainty analysis. From the domain characterization, I identified the most common metrics applied for uncertainty quantification and discussed the current visualization applications of these methods. Based on the interviews with domain experts, I characterized the background and intents of the experts when performing uncertainty analysis. This enables me to characterize domain needs that are currently underrepresented or unsupported in the literature. Finally, I developed a new framework for visualizing uncertainty in climate ensembles. / Dissertation/Thesis / Masters Thesis Computer Science 2016

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this study, a suite of feature-based techniques is developed to address these issues. First, a framework of feature-level uncertainty visualization is presented to study the uncertainty of the features in scalar and vector data. The uncertainty in the number and locations of features such as sinks or sources of vector fields are referred to as feature-level uncertainty while the uncertainty in the numerical values of the data is referred to as data-level uncertainty. The features of different ensemble members are indentified and correlated. The feature-level uncertainties are expressed as the transitions between corresponding features through new elliptical glyphs. Second, an interactive visualization tool for exploring scalar data with data-level and two types of feature-level uncertainties — contour-level and topology-level uncertainties — is developed. To avoid visual cluttering and occlusion, the uncertainty information is attached to a contour tree instead of being integrated with the visualization of the data. An efficient contour tree-based interface is designed to reduce users’ workload in viewing and analyzing complicated data with uncertainties and to facilitate a quick and accurate selection of prominent contours. This thesis advances the current uncertainty studies with an in-depth investigation of the feature-level uncertainties and an exploration of topology tools for effective and interactive uncertainty visualizations. With quantified representation and interactive capability, feature-based visualization helps people gain new insights into the uncertainties of their data, especially the uncertainties of extracted features which otherwise would remain unknown with the visualization of only data-level uncertainties.

Electronically collected animal movement data has been analyzed either statistically or visually using generic geographical information systems. The area of statistical analysis in this field has made progress over the last decade. However, visualizing the movement and behavior remains an open research problem. We have designed and implemented an interactive visualization system, MarineVis, to visualize geospatial uncertainty in the trajectories of marine animals. Using MarineVis, researchers are able to access, analyze and visualize marine animal data and oceanographic data with a variety of approaches. In this thesis, we discuss the MarineVis design structure, rendering techniques, and other visualization techniques which are used by existing software such as IDV to which we compare and contrast the visualization features of our system. Finally, directions of future work related to MarineVis are proposed which will inspire others to further study the challenging but amazingly interesting and exciting research field of marine visualization. / Marine animal movement is a fundamental yet poorly understood process. One of the reasons is because our understanding of movement is affected by the measurement error during the observation and process noise. Differentiating real movement behavior from observation error in data remains difficult and challenging. Methods that acknowledge uncertainty in movement pathways when estimating constantly changing animal movement have been lacking until this time. However with the arrival of state-space models, this problem is partially solved as SSMs acknowledge this problem by allowing unobservable true states to be estimated from data observed with errors which arise from imprecise observations. State-space models use Markov Chain Monte Carlo methods which generate samples from a distribution by constructing a Markov Chain where the current state only depends on the immediately preceding state. The task of fitting SSMs to data is challenging and requires large computational effort and expertise in statistics. With the arrival of the WinBUGs software, this formidable task becomes relatively easy. Though using the WinBUGs software researchers try to visualize the tracks and behaviors, new problems appear. One of the problems is that when marine animals come back to certain places or animals' tracks cross each other several times, the tracks become cluttered and users are not able to understand the direction. Another problem of visualizing the confidence intervals generated using SSMs is that images generated using other systems are static in nature and therefore lack interactivity. Information becomes cluttered when too much data appear. Users are not able to differentiate tracks, confidence intervals or the information they would like to visualize. Acknowledging these, we have designed and implemented an interactive visualization system, MarineVis, where these problems are overcome. Using our system the confidence intervals generated using the SSMs, can be visualized more clearly and the direction of the turtle tracks can be understood easily. Our system does not occlude the underlying terrain as much because the glyphs are localized at the sample points rather than being spread out around the entire path. Our system encodes both direction and position rather than just position. Users can interactively limit the view of data points as a subset of available data points on a path, in clustered regions, to reduce congestion, and can animate the progression of the animal along its trajectory which is absent in existing approaches. All these results are visualized over NASA World Wind maps that facilitates the understanding of the tracks.

abstract: Decision makers contend with uncertainty when working through complex decision problems. Yet uncertainty visualization, and tools for working with uncertainty in GIS, are not widely used or requested in decision support. This dissertation suggests a disjoint exists between practice and research that stems from differences in how visualization researchers conceptualize uncertainty and how decision makers frame uncertainty. To bridge this gap between practice and research, this dissertation explores uncertainty visualization as a means for reframing uncertainty in geographic information systems for use in policy decision support through three connected topics. Initially, this research explores visualizing the relationship between uncertainty and policy outcomes as a means for incorporating policymakers' decision frames when visualizing uncertainty. Outcome spaces are presented as a method to represent the effect of uncertainty on policy outcomes. This method of uncertainty visualization acts as an uncertainty map, representing all possible outcomes for specific policy decisions. This conceptual model incorporates two variables, but implicit uncertainty can be extended to multivariate representations. Subsequently, this work presented a new conceptualization of uncertainty, termed explicit and implicit, that integrates decision makers' framing of uncertainty into uncertainty visualization. Explicit uncertainty is seen as being separate from the policy outcomes, being described or displayed separately from the underlying data. In contrast, implicit uncertainty links uncertainty to decision outcomes, and while understood, it is not displayed separately from the data. The distinction between explicit and implicit is illustrated through several examples of uncertainty visualization founded in decision science theory. Lastly, the final topic assesses outcome spaces for communicating uncertainty though a human subject study. This study evaluates the effectiveness of the implicit uncertainty visualization method for communicating uncertainty for policy decision support. The results suggest that implicit uncertainty visualization successfully communicates uncertainty in results, even though uncertainty is not explicitly shown. Participants also found the implicit visualization effective for evaluating policy outcomes. Interestingly, participants also found the explicit uncertainty visualization to be effective for evaluating the policy outcomes, results that conflict with prior research. / Dissertation/Thesis / Ph.D. Geography 2013

Data is virtually always uncertain in one way or another. Yet, uncertainty information is not routinely included in visualizations and, outside of simple 1D diagrams, there is no established way to do it. One big issue is to find a method that shows the uncertainty without completely cluttering the display. A second important question that needs to be solved, is how uncertainty and interpolation interact. Interpolated values are inherently uncertain, because they are heuristically estimated values – not measurements. But how much more uncertain are they? How can this effect be modeled? In this thesis, we introduce Gaussian processes, a statistical framework that allows for the smooth interpolation of data with heteroscedastic uncertainty through regression. Its theoretical background makes it a convincing method to analyze uncertain data and create a model of the underlying phenomenon and, most importantly, the uncertainty at and in-between the data points. For this reason, it is already popular in the GIS community where it is known as Kriging but has applications in machine learning too. In contrast to traditional interpolation methods, Gaussian processes do not merely create a surface that runs through the data points, but respect the uncertainty in them. This way, noise, errors or outliers in the data do not disturb the model inappropriately. Most importantly, the model shows the variance in the interpolated values, which can be higher but also lower than that of its neighboring data points, providing us with a lot more insight into the quality of our data and how it influences our uncertainty! This enables us to use uncertainty information in algorithms that need to interpolate between data points, which includes almost all visualization algorithms.

AI-generated subtitles have recently started to automate the process of subtitling with automatic speech recognition. However, people may not perceive that the transcription is based on probabilities and may entail errors. For news that is broadcast live may this be controversial and cause misinterpretation. A user-centered design approach was performed investigating three possible solutions towards visualizing transcription uncertainties in real-time presentation. Based on the user needs, one proposed solution was used in a qualitative comparison with AI- generated subtitles without visualizations. The results suggest that visualization of uncertainties support users’ interpretation of AI-generated subtitles and helps to identify possible errors. However, it does not improve the transcription intelligibility. The result also suggests that unnoticed transcription errors during news broadcast is perceived as critical and decrease trust towards the news. Uncertainty visualizations may increase trust and prevent the risk of misinterpretation with important information.

Exploring multi-dimensional hierarchical data is a long-standing problem present in a wide range of fields such as bioinformatics, software systems, social sciences and business intelligence. While each hierarchical dimension within these data structures can be explored in isolation, critical information lies in the relationships between dimensions. Existing approaches can either simultaneously visualize multiple non-hierarchical dimensions, or only one or two hierarchical dimensions. Yet, the challenge of visualizing multi-dimensional hierarchical data remains open. To address this problem, we developed a novel data visualization approach -- Parallel Hierarchies -- that we demonstrate on a real-life SAP SE product called SAP Product Lifecycle Costing. The starting point of the research is a thorough customer-driven requirement engineering phase including an iterative design process. To avoid restricting ourselves to a domain-specific solution, we abstract the data and tasks gathered from users, and demonstrate the approach generality by applying Parallel Hierarchies to datasets from bioinformatics and social sciences. Moreover, we report on a qualitative user study conducted in an industrial scenario with 15 experts from 9 different companies. As a result of this co-innovation experience, several SAP customers requested a product feature out of our solution. Moreover, Parallel Hierarchies integration as a standard diagram type into SAP Analytics Cloud platform is in progress. This thesis further introduces different uncertainty representation methods applicable to Parallel Hierarchies and in general to flow diagrams. We also present a visual comparison taxonomy for time-series of hierarchically structured data with one or multiple dimensions. Moreover, we propose several visual solutions for comparing hierarchies employing flow diagrams. Finally, after presenting two application examples of Parallel Hierarchies on industrial datasets, we detail two validation methods to examine the effectiveness of the visualization solution. Particularly, we introduce a novel design validation table to assess the perceptual aspects of eight different visualization solutions including Parallel Hierarchies.:1 Introduction 1.1 Motivation and Problem Statement 1.2 Research Goals 1.3 Outline and Contributions 2 Foundations of Visualization 2.1 Information Visualization 2.1.1 Terms and Definition 2.1.2 What: Data Structures 2.1.3 Why: Visualization Tasks 2.1.4 How: Visualization Techniques 2.1.5 How: Interaction Techniques 2.2 Visual Perception 2.2.1 Visual Variables 2.2.2 Attributes of Preattentive and Attentive Processing 2.2.3 Gestalt Principles 2.3 Flow Diagrams 2.3.1 Classifications of Flow Diagrams 2.3.2 Main Visual Features 2.4 Summary 3 Related Work 3.1 Cross-tabulating Hierarchical Categories 3.1.1 Visualizing Categorical Aggregates of Item Sets 3.1.2 Hierarchical Visualization of Categorical Aggregates 3.1.3 Visualizing Item Sets and Their Hierarchical Properties 3.1.4 Hierarchical Visualization of Categorical Set Aggregates 3.2 Uncertainty Visualization 3.2.1 Uncertainty Taxonomies 3.2.2 Uncertainty in Flow Diagrams 3.3 Time-Series Data Visualization 3.3.1 Time & Data 3.3.2 User Tasks 3.3.3 Visual Representation 3.4 Summary ii Contents 4 Requirement Engineering Phase 4.1 Introduction 4.2 Environment 4.2.1 The Product 4.2.2 The Customers and Development Methodology 4.2.3 Lessons Learned 4.3 Visualization Requirements for Product Costing 4.3.1 Current Visualization Practice 4.3.2 Visualization Tasks 4.3.3 Data Structure and Size 4.3.4 Early Visualization Prototypes 4.3.5 Challenges and Lessons Learned 4.4 Data and Task Abstraction 4.4.1 Data Abstraction 4.4.2 Task Abstraction 4.5 Summary and Outlook 5 Parallel Hierarchies 5.1 Introduction 5.2 The Parallel Hierarchies Technique 5.2.1 The Individual Axis: Showing Hierarchical Categories 5.2.2 Two Interlinked Axes: Showing Pairwise Frequencies 5.2.3 Multiple Linked Axes: Propagating Frequencies 5.2.4 Fine-tuning Parallel Hierarchies through Reordering 5.3 Design Choices 5.4 Applying Parallel Hierarchies 5.4.1 US Census Data 5.4.2 Yeast Gene Ontology Annotations 5.5 Evaluation 5.5.1 Setup of the Evaluation 5.5.2 Procedure of the Evaluation 5.5.3 Results from the Evaluation 5.5.4 Validity of the Evaluation 5.6 Summary and Outlook 6 Visualizing Uncertainty in Flow Diagrams 6.1 Introduction 6.2 Uncertainty in Product Costing 6.2.1 Background 6.2.2 Main Causes of Bad Quality in Costing Data 6.3 Visualization Concepts 6.4 Uncertainty Visualization using Ribbons 6.4.1 Selected Visualization Techniques 6.4.2 Study Design and Procedure 6.4.3 Results 6.4.4 Discussion 6.5 Revised Visualization Approach using Ribbons 6.5.1 Application to Sankey Diagram 6.5.2 Application to Parallel Sets 6.5.3 Application to Parallel Hierarchies 6.6 Uncertainty Visualization using Nodes 6.6.1 Visual Design of Nodes 6.6.2 Expert Evaluation 6.7 Summary and Outlook 7 Visual Comparison Task 7.1 Introduction 7.2 Comparing Two One-dimensional Time Steps 7.2.1 Problem Statement 7.2.2 Visualization Design 7.3 Comparing Two N-dimensional Time Steps 7.4 Comparing Several One-dimensional Time Steps 7.5 Summary and Outlook 8 Parallel Hierarchies in Practice 8.1 Application to Plausibility Check Task 8.1.1 Plausibility Check Process 8.1.2 Visual Exploration of Machine Learning Results 8.2 Integration into SAP Analytics Cloud 8.2.1 SAP Analytics Cloud 8.2.2 Ocean to Table Project 8.3 Summary and Outlook 9 Validation 9.1 Introduction 9.2 Nested Model Validation Approach 9.3 Perceptual Validation of Visualization Techniques 9.3.1 Design Validation Table 9.3.2 Discussion 9.4 Summary and Outlook 10 Conclusion and Outlook 10.1 Summary of Findings 10.2 Discussion 10.3 Outlook A Questionnaires of the Evaluation B Survey of the Quality of Product Costing Data C Questionnaire of Current Practice Bibliography