Christian Tominski

Visualization of Time-Oriented Data

Time is an exceptional dimension that is common to many application domains such as medicine, engineering, business, or science. Due to the distinct characteristics of time, appropriate visual and analytical methods are required to explore and analyze them. This book starts with an introduction to visualization and historical examples of visual representations. At its core, the book presents and discusses a systematic view of the visualization of time-oriented data along three key questions: what is being visualized (data), why something is visualized (user tasks), and how it is presented (visual representation). To support visual exploration, interaction techniques and analytical methods are required that are discussed in separate chapters. A large part of this book is devoted to a structured survey of 101 different visualization techniques as a reference for scientists conducting related research as well as for practitioners seeking information on how their time-oriented data can best be visualized.

Space, time and visual analytics

Visual analytics aims to combine the strengths of human and electronic data processing. Visualisation, whereby humans and computers cooperate through graphics, is the means through which this is achieved. Seamless and sophisticated synergies are required for analysing spatio-temporal data and solving spatio-temporal problems. In modern society, spatio-temporal analysis is not solely the business of professional analysts. Many citizens need or would be interested in undertaking analysis of information in time and space. Researchers should find approaches to deal with the complexities of the current data and problems and find ways to make analytical tools accessible and usable for the broad community of potential users to support spatio-temporal thinking and contribute to solving a large range of problems.

Visualizing time-oriented data-A systematic view

The analysis of time-oriented data is an important task in many application scenarios. In recent years, a variety of techniques for visualizing such data have been published. This variety makes it difficult for prospective users to select methods or tools that are useful for their particular task at hand. In this article, we develop and discuss a systematic view on the diversity of methods for visualizing time-oriented data. With the proposed categorization we try to untangle the visualization of time-oriented data, which is such an important concern in Visual Analytics. The categorization is not only helpful for users, but also for researchers to identify future tasks in Visual Analytics.

Visual Methods for Analyzing Time-Oriented Data

Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose, we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.

Stacking-Based Visualization of Trajectory Attribute Data

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well.

3D information visualization for time dependent data on maps

The visual analysis of time dependent data is an essential task in many application fields. However, visualizing large time dependent data collected within a spatial context is still a challenging task. In this paper, we therefore describe an approach for visualizing spatio-temporal data on maps. The approach is based on two commonly used concepts: 3D information visualization and information hiding. These concepts are realized by means of novel embeddings of 3D icons into a map display for representing spatio-temporal data, and an integration of event-based methods for reducing the amount of information to be represented. Our approach is capable of visualizing multiple time dependent attributes on maps, and of emphasizing the characteristics constituted by either linear or cyclic temporal dependencies.

Axes-based visualizations with radial layouts

In the analysis of multidimensional data sets questions involving detection of extremal events, correlations, patterns and trends play an increasingly important role in a variety of applications. Axes-based visualizations like Parallel or Star Coordinates are useful tools for the analysis of multidimensional data sets. In this paper, we present several interactive axes, which can be used to analyze data in an intuitive manner. Furthermore, we present two novel radial visual arrangements of such axes - the TimeWheel and the MultiComb. They focus on data sets with one variable of reference. TimeWheel and MultiComb in combination with interactive axes are part of an interactive framework called VisAxes, which can be used for enhanced multidimensional data browsing and analysis.

Fisheye Tree Views and Lenses for Graph Visualization

We present interactive visual aids to support the exploration and navigation of graph layouts. They include fisheye tree views and composite lenses. These views provide, in an integrated manner, overview+detail and focus+context. Fisheye tree views are novel applications of the well known fisheye distortion technique. They facilitate the exploration of the hierarchy trees associated with clustered graphs. Composite lenses are the result of the integration of several lens techniques. They facilitate the display of local graph information that may be otherwise difficult to grasp in large and dense graph layouts

Technical Section: CGV-An interactive graph visualization system

Previous work on graph visualization has yielded a wealth of efficient graph analysis algorithms and expressive visual mappings. To support the visual exploration of graph structures, a high degree of interactivity is required as well. We present a fully implemented graph visualization system, called CGV (Coordinated Graph Visualization), whose particular emphasis is on interaction. The system incorporates several interactive views that address different aspects of graph visualization. To support different visualization tasks, view ensembles can be created dynamically with the help of a flexible docking framework. Several novel techniques, including enhanced dynamic filtering, graph lenses, and edge-based navigation are presented. The main graph canvas interactions are augmented with several visual cues, among which the infinite grid and the radar view are novel. CGV provides a history mechanism that allows for undo/redo of interaction. CGV is a general system with potential application in many scenarios. It has been designed as a dual-use system that can run as a stand-alone application or as an applet in a web browser. CGV has been used to evaluate graph clustering results, to navigate topological structures of neuronal systems, and to perform analysis of some time-varying graphs.

A Multi-Threading Architecture to Support Interactive Visual Exploration

During continuous user interaction, it is hard to provide rich visual feedback at interactive rates for datasets containing millions of entries. The contribution of this paper is a generic architecture that ensures responsiveness of the application even when dealing with large data and that is applicable to most types of information visualizations. Our architecture builds on the separation of the main application thread and the visualization thread, which can be cancelled early due to user interaction. In combination with a layer mechanism, our architecture facilitates generating previews incrementally to provide rich visual feedback quickly. To help avoiding common pitfalls of multi-threading, we discuss synchronization and communication in detail. We explicitly denote design choices to control trade-offs. A quantitative evaluation based on the system VI S P L ORE shows fast visual feedback during continuous interaction even for millions of entries. We describe instantiations of our architecture in additional tools.