Zhao Geng

Angular Histograms: Frequency-Based Visualizations for Large, High Dimensional Data

Parallel coordinates is a popular and well-known multivariate data visualization technique. However, one of their inherent limitations has to do with the rendering of very large data sets. This often causes an overplotting problem and the goal of the visual information seeking mantra is hampered because of a cluttered overview and non-interactive update rates. In this paper, we propose two novel solutions, namely, angular histograms and attribute curves. These techniques are frequency-based approaches to large, high-dimensional data visualization. They are able to convey both the density of underlying polylines and their slopes. Angular histogram and attribute curves offer an intuitive way for the user to explore the clustering, linear correlations and outliers in large data sets without the over-plotting and clutter problems associated with traditional parallel coordinates. We demonstrate the results on a wide variety of data sets including real-world, high-dimensional biological data. Finally, we compare our methods with the other popular frequency-based algorithms.

ShakerVis: Visual analysis of segment variation of German translations of Shakespeare’s Othello

William Shakespeare is one of the world’s greatest writers. His plays have been translated into every major living language. In some languages, his plays have been retranslated many times. These translations and retranslations have evolved for about 250 years. Studying variations in translations of world cultural heritage texts is of cross-cultural interest for arts and humanities researchers. The variations between retranslations are due to numerous factors, including the differing purposes of translations, genetic relations, cultural and intercultural influences, rivalry between translators and their varying competence. A team of Digital Humanities researchers has collected an experimental corpus of 55 different German retranslations of Shakespeare’s play, Othello. The retranslations date between 1766 and 2010. A sub-corpus of 32 retranslations has been prepared as a digital parallel corpus. We would like to develop methods of exploring patterns in variation between different translations. In this article, we develop an interactive focus + context visualization system to present, analyse and explore variation at the level of user-defined segments. From our visualization, we are able to obtain an overview of the relationships of similarity between parallel segments in different versions. We can uncover clusters and outliers at various scales, and a linked focus view allows us to further explore the textual details behind these findings. The domain experts who are studying this topic evaluate our visualizations, and we report their feedback. Our system helps them better understand the relationships between different German retranslations of Othello and derive some insight.

Visualization of Large, Time-Dependent, Abstract Data with Integrated Spherical and Parallel Coordinates

Parallel coordinates is one of the most popular and widely used visualization techniques for large, high dimensional data. Often, data attributes are visualized on individual axes with polylines joining them. However, some data attributes are more naturally represented with a spherical coordinate system. We present a novel coupling of parallel coordinates with spherical coordinates, enabling the visualization of vector and multi-dimensional data. The spherical plot is integrated as if it is an axis in the parallel coordinate visualization. This hybrid visualization benefits from enhanced visual perception, representing vector data in a more natural spatial domain and also reducing the number of parallel axis within the parallel coordinates plot. This raises several challenges which we discuss and provide solutions to, such as, visual clutter caused by over plotting and the computational complexity of visualizing large abstract, time-dependent data. We demonstrate the results of our work-in-progress visualization technique using biological animal tracking data of a large, multi-dimensional, time-dependent nature, consisting of tri-axial accelerometry samples as well as several additional attributes. In order to understand marine wildlife behavior, the acceleration vector is reconstructed in spherical coordinates and visualized alongside with the other data attributes to enable exploration, analysis and presentation of marine wildlife behavior.

Visualizing translation variation: Shakespeare's Othello

Recognized as great works of world literature, Shakespeares poems and plays have been translated into dozens of languages for over 300 years. Also, there are many re-translations into the same language, for example, there are more than 60 translations of Othello into German. Every translation is a different interpretation of the play. These large quantities of translations reflect changing culture and express individual thought by the authors. They demonstrate wide connections between different world regions today, and reveal a retrospective view of their cultural, intercultural, and linguistic histories. Researchers from Arts and Humanities at Swansea University are collecting a large number of translations ofWilliamShakespeares Othello. In this paper, we have developed an interactive visualization system to present, analyze and explore the variations among these different translations. Our system is composed of two parts: the structure-aware Treemap for document selection and meta data analysis, and Focus + Context parallel coordinates for in-depth document comparison and exploration. In particular, we want to learn more about which content varies highly with each translation, and which content remains stable. We also want to form hypotheses as to the implications behind these variations. Our visualization is evaluated by the domain experts from Arts and Humanities.

Force-Directed Parallel Coordinates

Parallel coordinates are a well-known and valuable technique for the analysis and visualization of high dimensional data sets. However, while Inselberg emphasizes that the strength of parallel coordinates as a methodology is rooted in exploration and interactivity, the set of interaction techniques is currently limited. Axes can be re-ordered and brushing (simple, angular or multi-dimensional) can be performed. In this paper, we propose a force-directed algorithm and related interaction techniques to support the exploration of parallel coordinate plots through a physical metaphor. Our parallel-coordinates visualization offers novel user interaction beyond the standard techniques by allowing the user to rotate the axis according to forcedirected polylines. The new interaction provides the user with a more immersive experience for data exploration that results in greater intuition of the data, especially in cases where many polylines overlap. We demonstrate our approach, then present the results of a qualitative evaluation of the system.

Design and Implementation of a System for Interactive Higher Dimensional Vector Field Visualization

Although the challenge of 2D flow visualization is deemed virtually solved as a result of the tremendous amount of effort invested into this problem, higher-dimensional flow visualization, (e.g. the visualization of flow on surfaces in 3D (2.5D), volumetric flow (3D), and flow with multiple attributes (nD) ), still holds many challenges which need to be addressed. We describe the design and implementation of a generic framework incorporating a selection of related scientific and information visualization techniques which are interactively integrated to provide tools for effective visualization of the higher dimensional CFD (Computational Fluid Dynamics) simulation data. In contrast to the most research prototypes, the system we present handles real-world, unstructured simulation data. Our framework involves direct, vector field clustering and geometric flow visualization techniques. Information visualization approaches, such as a tabular histogram, velocity histogram, and parallel coordinate plot are also integrated. In order to enable a smooth and efficient user interaction, these visualization options are systematically combined on a multi-threading platform which ensures responsiveness even when processing large data.

Visualization of flow past a marine turbine: the information-assisted search for sustainable energy

Interest in renewable, green, and sustainable energy has risen sharply in recent years. The use of marine turbines to extract kinetic energy from the tidal current is gaining popularity. CFD modeling is carried out to investigate the surrounding flow behavior and thus develop effective marine turbine systems. However, visualizing the simulation results remains a challenging task for engineers. In this paper, we develop, explore and present customized visualization techniques in order to help engineers gain a fast overview and intuitive insight into the flow past the marine turbine. The system exploits multiple-coordinated information-assisted views of the CFD simulation data. Our application consists of a tabular histogram, velocity histogram, parallel coordinate plot, streamline plot and spatial views. Information-based streamline seeding is used to investigate the behavior of the flow deemed interesting to the engineer. Specialized, application-specific information based on swirling flow is derived and visualized in order to evaluate turbine blade design. To demonstrate the usage of our system, a selection of specialized case scenarios designed to answer the core questions brought out by engineers is described. We also report feedback on our system from CFD experts researching marine turbine simulations.

Markov Chain Driven Multi-Dimensional Visual Pattern Analysis with Parallel Coordinates

Parallel coordinates is a widely used visualization technique for presenting, analyzing and exploring multidimensional data. However, like many other visualizations, it can suffer from an overplotting problem when rendering large data sets. Until now, quite a few methods are proposed to discover and illustrate the major data trends in cluttered parallel coordinates. Among them, frequency-based approaches using binning and histograms are widely adopted. The traditional binning method, which records line-segment frequency, only considers data in a two-dimensional subspace, as a result, the multi-dimensional features are not taken into account for trend and outlier analysis. Obtaining a coherent binned representation in higher dimensions is challenging because multidimensional binning can suffer from the curse of dimensionality. In this paper, we utilize the Markov Chain model to compute an n-dimensional joint probability for each data tuple based on a two-dimensional binning method. This probability value can be utilized to guide the user for selection and brushing. We provide various interaction techniques for the user to control the parameters during the brushing process. Filtered data with a high probability measure often explicitly illustrates major data trends. In order to scale to large data sets, we also propose a more precise angular representation for angular histograms to depict the density of the brushed data trends. We demonstrate our methods and evaluate the results on a wide variety of data sets, including real-world, high-dimensional biological data.

Visual Analysis of Hierarchical Management Data

The sales force management data at Thomson Reuters often contains multiple hierarchies and dependencies. Conventional business graphics are not sufficient for analyzing, presenting and exploring such data sets. The Treemap is effective for depicting hierarchical data, although it lacks structural clarity. Thus, distinguishing different levels within the treemap is difficult. In this abstract, we develop an interactive system for business analysts which enables structure-aware visualization of hierarchical data using multiple coordinated views. The design of this system includes hierarchy extraction, structure tracings and the direct manipulation of the treemap. We demonstrate our result on various sales force management data sets from Thomson Reuters.