Thomas Nocke

A Design Space of Visualization Tasks

Knowledge about visualization tasks plays an important role in choosing or building suitable visual representations to pursue them. Yet, tasks are a multi-faceted concept and it is thus not surprising that the many existing task taxonomies and models all describe different aspects of tasks, depending on what these task descriptions aim to capture. This results in a clear need to bring these different aspects together under the common hood of a general design space of visualization tasks, which we propose in this paper. Our design space consists of five design dimensions that characterize the main aspects of tasks and that have so far been distributed across different task descriptions. We exemplify its concrete use by applying our design space in the domain of climate impact research. To this end, we propose interfaces to our design space for different user roles (developers, authors, and end users) that allow users of different levels of expertise to work with it.

A History Mechanism for Visual Data Mining

A major challenge of current visualization and visual data mining (VDM) frameworks is to support users in the orientation in complex visual mining scenarios. An important aspect to increase user support and user orientation is to use a history mechanism that, first of all, provides un- and redoing functionality. In this paper, we present a new approach to include such history functionality into a VDM framework. Therefore, we introduce the theoretical background, outline design and implementation aspects of a history management unit, and conclude with a discussion showing the usefulness of our history management in a VDM framework

Visual exploration and evaluation of climate-related simulation data

Large, heterogeneous volumes of simulation data are calculated and stored in many disciplines, e.g. in climate and climate impact research. To gain insight, current climate analysis applies statistical methods and model sensitivity analyzes in combination with standard visualization techniques. However, there are some obstacles for researchers in applying the full functionality of sophisticated visualization, exploiting the available interaction and visualization functionality in order to go beyond data presentation tasks. In particular, there is a gap between available and actually applied multi-variate visualization techniques. Furthermore, visual data comparison of simulation (and measured) data is still a challenging task. Consequently, this paper introduces a library of visualization techniques, tailored to support exploration and evaluation of climate simulation data. These techniques are integrated into the easy-to-use visualization framework SimEnvVis - designed as a front-end user interface to a simulation environment - which provides a high level of user support generating visual representations.

Information Visualization in Climate Research

Much of the work conducted in climate research involves large and heterogeneous datasets with spatial and temporal references. This makes climate research an interesting application area for visualization. However, the application of interactive visual methods to assist in gaining insight into climate data is still hampered for climate research scientists, who are usually not visualization experts. In this paper, we report on a survey that we conducted to evaluate the application of interactive visualization methods and to identify the problems related to establishing such methods in scientific practice. The feedback from 76 participants shows clearly that state-of-the-art techniques are rarely applied and that integrating existing solutions smoothly into the scientists workflow is problematic. We have begun to change this and present first results that illustrate how interactive visualization tools can be successfully applied to accomplish climate research tasks. As a concrete example, we describe the visualization of climate networks and its benefits for climate impact research.

Methods for the visualization of clustered climate data

SummaryIncreasing amounts of large climate data require new analysis techniques. The area of data mining investigates new paradigms and methods including factors like scalability, flexibility and problem abstraction for large data sets. The field of visual data mining in particular offers valuable methods for analyzing large amounts of data intuitively. In this paper we describe our approach of integrating cluster analysis and visualization methods for the exploration of climate data. We integrated cluster algorithms, appropriate visualization techniques and sophisticated interaction paradigms into a general framework.

Icon-based visualization using mosaic metaphors

This paper introduces a new approach to extend icon-based visualization methods by using a mosaic-based paradigm. We discuss how image metaphors closely related to the application domain can be applied for icon-based representations. Therefore, we enhance visualizations by well-known image mosaic techniques, such as image layouts, image selection and color adaption. Furthermore, we present the results of our approach by discussing an example of a clustered real-world climate data set.

Supporting presentation and discussion of visualization results in smart meeting rooms

Visualization has become an accepted tool to support the process of gaining insight into data. Current visualization research mainly focuses on exploratory or confirmatory visualization taking place in classic workplace settings. In this paper, we focus on the presentation and discussion of visualization results among domain experts, rather than on the generation of visual representations by visualization experts. We develop a visualization infrastructure for a novel kind of visualization environment labeled smart meeting room, which provides plenty of display space to present the visual information to be discussed. We describe the mechanisms needed to show multiple visualization views on multiple displays and to interact with the views across device boundaries. Our system includes methods to dynamically generate visualization views, to suggest suitable layouts of the views, and to enable interactive fine-tuning to accommodate the dynamically changing needs of the user (e.g., access to details on demand). The benefits for the users are illustrated by an application in the context of climate impact research.

Interactive Presentation of Geo-Spatial Climate Data in Multi-Display Environments

The visual analysis of complex geo-spatial data is a challenging task. Typically, different views are used to communicate different aspects. With changing topics of interest, however, novel views are required. This leads to dynamically changing presentations of multiple views. This paper introduces a novel approach to support such scenarios. It allows for a spontaneous incorporation of views from different sources and to automatically layout these views in a multi-display environment. Furthermore, we introduce an enhanced undo/redo mechanism for this setting, which records user interactions and, in this way, enables swift reconfigurations of displayed views. Hence, users can fluently switch the focus of visual analysis without extensive manual interactions. We demonstrate our approach by the particular use case of discussing geo-spatial climate data.

Integration of Cluster Analysis and Visualization Techniques for Visual Data Analysis

This Paper investigates the combination of numerical and visual exploration techniques focused on cluster analysis of multi-dimensional data. We describe our newdeveloped visualization approaches and selected clustering techniques along with major concepts of the integration and parameterization of these methods. The resulting frameworks and its major features will be discussed.

A systematic view on data descriptors for the visual analysis of tabular data

Visualization has become an important ingredient of data analysis, supporting users in exploring data and confirming hypotheses. At the beginning of a visual data analysis process, data characteristics are often assessed in an initial data profiling step. These include, for example, statistical properties of the data and information on the data’s well-formedness, which can be used during the subsequent analysis to adequately parametrize views and to highlight or exclude data items. We term this information data descriptors, which can span such diverse aspects as the data’s provenance, its storage schema, or its uncertainties. Gathered descriptors encapsulate basic knowledge about the data and can thus be used as objective starting points for the visual analysis process. In this article, we bring together these different aspects in a systematic form that describes the data itself (e.g. its content and context) and its relation to the larger data gathering and visual analysis process (e.g. its provenance and its utility). Once established in general, we further detail the concept of data descriptors specifically for tabular data as the most common form of structured data today. Finally, we utilize these data descriptors for tabular data to capture domain-specific data characteristics in the field of climate impact research. This procedure from the general concept via the concrete data type to the specific application domain effectively provides a blueprint for instantiating data descriptors for other data types and domains in the future.