Existing information visualization techniques are usually limited to the display of a few thousand items. This article describes new interactive techniques capable of handling a million items (effectively visible and manageable on screen). We evaluate the use of hardware-based techniques available with newer graphics cards, as well as new animation techniques and non-standard graphical features such as stereovision and overlap count.

Abstract — Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques.

This paper presents the results of a thorough user study that was performed to assess some features and the general usefulness of Semantic Depth of Field (SDOF). Based on these results, concrete hints are given on how SDOF can be used for visualization. SDOF was found to be a very effective means for guiding the viewer's attention and for giving him or her a quick overview of a data set. It can also very quickly be perceived, and therefore provides an efficient visual channel.

This report is broken down into four main sections, firstly giving the primary aims of the proposed research, followed by a review of background reading of present Information Visualisation (IV) techniques, which are categorised using an existing framework. This is followed by a description of the general visualisation problem we are concerned with plus a description of the specific area where such a visualisation could be of benefit. We then discuss the visualisation techniques that address situations that have the greatest similarity to our own problem, and explain why they still lack suitability for our purposes. Then, two prototypes that are under development are described, and the report finishes with a breakdown of proposed future work

Although luminance contrast plays a predominant role in motion perception, significant additional effects are introduced by chromatic contrasts. In this paper, relevant results from psychophysical and physiological research are described to clarify the role of color in motion detection. Interpreting these psychophysical experiments, we propose guidelines for the design of animated visualizations, and a calibration procedure that improves the reliability of visual motion representation. The guidelines are applied to examples from texture-based flow visualization, as well as graph and tree visualization.

Visualizing information in user interfaces to complex, large-scale systems is difficult due to visual fragmentation caused by an enormous amount of inter-related data distributed across multiple views. New display dimensions are required to help the user perceptually integrate and filter such spatially distributed and heterogeneous information. Motion holds promise in this regard as a perceptually efficient display dimension. It has long been known to have a strong grouping effect which suggest that it has potential for filtering and brushing techniques. However, there is little known about which properties of motion are most effective. We review the prior literature relating to the use of motion for display and discuss the requirements for how motion can be usefully applied to these problems, especially for visualizations incorporating multiple groups of data objects. We compared three shapes of motions in pairwise combinations: linear, circular and expansion/contraction. Combinations of linear directions were also compared to evaluate how great angular separation needs to be to enforce perceptual distinction. Our results showed that shape differentiation is more effective than directional differences (except for 90). Of the three shapes studied, circular demands the most attention. Angular separation must be 90 to be equally effective. These results suggest that motion can be usefully applied to both filtering and brushing. They also provide the beginnings of a vocabulary of simple motions that can be applied to information visualization.

We study the generation and visualization of residuals for detecting and identifying unseen faults using autoassociative models learned from process data. Least squares and kernel regression models are compared on the basis of their ability to describe the support of the data. Theoretical results show that kernel regression models are more appropriate in this sense. Moreover, experiments on vibration and current data from an asynchronous motor confirm the theory and yield more meaningful results.

Abstract. Information visualization and visual data mining leverage the human visual system to provide insight and understanding of unorganized data. In order to scale to massive sets of high dimensional data, simplification methods are needed, so as to select important dimensions and objects. Some machine learning algorithms try to solve those problems. We give in this paper an overview of information visualization and survey the links between this field and machine learning. 1

Metric studies are based on complex, voluminous and heterogeneous data. In order to obtain meaningful results, human guided analysis is therefore needed and can be achieved with information visualization methods. In this paper, we survey visualization methods traditionally used in informetrics and present recent achievements in this domain. We also outline some potentially interesting visualization tools from machine learning. 1.

In the field of agent-based modeling (ABM), visualizations play a key role in conveying, identifying and understanding the relevant behaviors of a model. However, few toolkits provide modelers graphic tools and visual cues for the design of efficient and communicative visualizations. In this paper, we review some technologies in the NetLogo ABM toolkit that improves its design of model visualizations. First, we present the rational for a change in its default colors space. Then, we describe an extension with new graphical primitives and dialogs to improve and expand color handling in NetLogo. After that, we show two feature prototypes, texture and transparency. Finally, we will discuss graphical cues to facilitate visualization tasks to perceive micro behaviors and macro behaviors. These new tools assist the user in the creation of more understandable and compelling visualizations.