Visual Support for the Modeling and Simulation of Cell Biological Processes

This dissertation aims at bringing information visualization closer to the demands of analytical problem solving for the specific domain of modeling and simulating cell biological systems. To this end, main segments of visual support in the domain are identified. For one of these segments, the visual analysis of simulation data, new concepts are developed. First, this includes the visualization of simulation data in the context of data generation. Second, new multiple view techniques for large and complex simulation data are introduced.Diese Arbeit verfolgt das Ziel, Informationsvisualisierung näher an die Anforderungen des Analyseprozesses heranzuführen, mit Blick auf die konkrete Anwendung der Modellierung und Simulation zellbiologischer Systeme. Dazu werden wesentliche Teilbereiche der visuellen Unterstützung identifiziert. Für den Teilbereich der visuellen Analyse von Simulationsdaten werden neue Konzepte entwickelt. Dies beinhaltet zum einen die Visualisierung von Simulationsdaten im Kontext der Datengenerierung. Zum anderen werden neue Multiple-View-Techniken für große und komplexe Simulationsdaten vorgestellt

Improving Interaction in Visual Analytics using Machine Learning

Interaction is one of the most fundamental components in visual analytical systems, which transforms people from mere viewers to active participants in the process of analyzing and understanding data. Therefore, fast and accurate interaction techniques are key to establishing a successful human-computer dialogue, enabling a smooth visual data exploration. Machine learning is a branch of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It has been utilized in a wide variety of fields, where it is not straightforward to develop a conventional algorithm for effectively performing a task. Inspired by this, we see the opportunity to improve the current interactions in visual analytics by using machine learning methods. In this thesis, we address the need for interaction techniques that are both fast, enabling a fluid interaction in visual data exploration and analysis, and also accurate, i.e., enabling the user to effectively select specific data subsets. First, we present a new, fast and accurate brushing technique for scatterplots, based on the Mahalanobis brush, which we have optimized using data from a user study. Further, we present a new solution for a near-perfect sketch-based brushing technique, where we exploit a convolutional neural network (CNN) for estimating the intended data selection from a fast and simple click-and-drag interaction and from the data distribution in the visualization. Next, we propose an innovative framework which offers the user opportunities to improve the brushing technique while using it. We tested this framework with CNN-based brushing and the result shows that the underlying model can be refined (better performance in terms of accuracy) and personalized by very little time of retraining. Besides, in order to investigate to which degree the human should be involved into the model design and how good the empirical model can be with a more careful design, we extended our Mahalanobis brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information, captured by kernel density estimation (KDE). Based on this work, we then provide a detailed comparison between empirical modeling and implicit modeling by machine learning (deep learning). Lastly, we introduce a new, machine learning based approach that enables the fast and accurate querying of time series data based on a swift sketching interaction. To achieve this, we build upon existing LSTM technology (long short-term memory) to encode both the sketch and the time series data in two networks with shared parameters. All the proposed interaction techniques in this thesis were demonstrated by application examples and evaluated via user studies. The integration of machine learning knowledge into visualization opens further possible research directions.Doktorgradsavhandlin

Casual Information Visualization on Exploring Spatiotemporal Data

The goal of this thesis is to study how the diverse data on the Web which are familiar to everyone can be visualized, and with a special consideration on their spatial and temporal information. We introduce novel approaches and visualization techniques dealing with different types of data contents: interactively browsing large amount of tags linking with geospace and time, navigating and locating spatiotemporal photos or videos in collections, and especially, providing visual supports for the exploration of diverse Web contents on arbitrary webpages in terms of augmented Web browsing

Visual analytics methods for retinal layers in optical coherence tomography data

Optical coherence tomography is an important imaging technology for the early detection of ocular diseases. Yet, identifying substructural defects in the 3D retinal images is challenging. We therefore present novel visual analytics methods for the exploration of small and localized retinal alterations. Our methods reduce the data complexity and ensure the visibility of relevant information. The results of two cross-sectional studies show that our methods improve the detection of retinal defects, contributing to a deeper understanding of the retinal condition at an early stage of disease.Die optische Kohärenztomographie ist ein wichtiges Bildgebungsverfahren zur Früherkennung von Augenerkrankungen. Die Identifizierung von substrukturellen Defekten in den 3D-Netzhautbildern ist jedoch eine Herausforderung. Wir stellen daher neue Visual-Analytics-Methoden zur Exploration von kleinen und lokalen Netzhautveränderungen vor. Unsere Methoden reduzieren die Datenkomplexität und gewährleisten die Sichtbarkeit relevanter Informationen. Die Ergebnisse zweier Querschnittsstudien zeigen, dass unsere Methoden die Erkennung von Netzhautdefekten in frühen Krankheitsstadien verbessern

Exploration and Analysis of Information Visualization Techniques Applied to the TeachEngineering Digital Library

The rapid development of computing technologies in recent decades has allowed for data to be generated at unprecedented rates. As a result, users are challenged with the task of finding the precise information that they are looking for. Visualization techniques provide the ability to view and analyze data in different ways, helping users to find what they are looking for and gain additional insights. TeachEngineering is a digital library system where K-12 educators can search for high-quality lesson plans that meet national and state-specific educational standards. Users have several different types of criteria to search by, including: grade level, cost, time required, group size, keywords, source state, and educational standards. Being able to see how individual documents compare to specific variables can help identify additional results that are worthy of user consideration. Surveying visualization techniques, specific methods (scatterplots, parallel coordinates, star glyphs and hyperbolic trees) are qualitatively analyzed with regards to how they could be applied to TeachEngineering search query results. Each visualization type has advantages and limitations, and no single technique can be considered the definitive solution. Recommendations on conducting future experiments and research are included

Interactive Visual Analysis of Translations

This thesis is the result of a collaboration with the College of Arts and Humanities at Swansea University. The goal of this collaboration is to design novel visualization techniques to enable digital humanities scholars to explore and analyze parallel translations. To this end, chapter 2 introduces the first survey of surveys on text visualization which reviews all of the surveys and state-of-the-art reports on text visualization techniques, classifies them, provides recommendations, and discusses reported challenges.Following this, we present three visual interactive designs that support the typical digital humanities scholars workflow. In Chapter 4, we present VNLP, a visual, interactive design that enables users to explicitly observe the NLP pipeline processes and update the parameters at each processing stage. Chapter 5 presents AlignVis, a visual tool that provides a semi-automatic alignment framework to build a correspondence between multiple translations. It presents the results of using text similarity measurements and enables the user to create, verify, and edit alignments using a novel visual interface. Chapter 6 introduce TransVis, a novel visual design that supports comparison of multiple parallel translations. It incorporates customized mechanisms for rapid and interactive filtering and selection of a large number of German translations of Shakespeare’s Othello. All of the visual designs are evaluated using examples, detailed observations, case studies, and/or domain expert feedback from a specialist in modern and contemporary German literature and culture.Chapter 7 reports our collaborative experience and proposes a methodological workflow to guide such interdisciplinary research projects. This chapter also includes a summary of outcomes and lessons learned from our collaboration with the domain expert. Finally, Chapter 8 presents a summary of the thesis and future work directions