Designing and evaluating notification systems represents an emerging challenge in the study of human--computer interaction. Users rely on notification systems to present potentially interruptive information in an efficient and effective manner to enable appropriate reaction and comprehension. Little is known about the effects of these systems on ongoing computer tasks. As the research community strives to understand information design suitable for opposing usage goals, few existing efforts lend themselves to extensibility.

This article provides a first look at an extensible philosophy for studying other instances of multitasking or collaborative performance. We argue that the models and framework presented here will improve the HCI community's ability to classify and evaluate existing and emerging notification systems, as well as to catalog information and interaction design guidelines and lessons learned in a cohesive, collective manner. In the next section, we present a more thorough overview of notification systems appearing in recent literature and itemize general user goals, providing motivation and background material for the model we present in Section 3

We propose an approach for complex software analysis based on visualization. Our work is motivated by the fact that in spite of years of research and practice, software development and maintenance are still time and resource consuming, and high-risk activities. The most important reason in our opinion is the complexity of many phenomena related to software, such as its evolution and its reliability. In fact, there is very little theory explaining them. Today, we have a unique opportunity to empirically study these phenomena, thanks to large sets of software data available through open-source programs and open repositories. Automatic analysis techniques, such as statistics and machine learning, are usually limited when studying phenomena with unknown or poorlyunderstood influence factors. We claim that hybrid techniques that combine automatic analysis with human expertise through visualization are excellent alternatives to them. In this paper, we propose a visualization framework that supports quality analysis of large-scale software systems. We circumvent the problem of size by exploiting perception capabilities of the human visual system. Categories and Subject Descriptors D.2.8 [Software Engineering]: Metrics—complexity measures, product metrics; D.1.5 [Programming Techniques]:

Scientific visualization represents information as images that let us explore, discover, analyze, and validate large collections of data. Much research in this area is dedicated to designing effective visualizations that support specific analysis needs. Recently, though, we’ve considered visualizations from another angle. We’ve started asking, Are visualizations beautiful? Can we consider visualizations works of art? You might expect answers to these questions to vary widely depending on an individual’s interpretation of what it means to be artistic. We believe that the issues of effectiveness and aesthetics may not be as independent as they seem initially. We can learn much from studying two related disciplines—human psychophysics and art theory and history. Human psychophysics teaches us

An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element’s attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the lowlevel human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.

This paper describes a new method for visualizing complex information spaces as painted images. Scientific visualization converts data into pictures that allow viewers to “see ” trends, relationships, and patterns. We introduce a formal definition of the correspondence between traditional visualization techniques and painterly styles from the Impressionist art movement. This correspondence allows us to apply perceptual guidelines from visualization to control the presentation of information in a computer-generated painting. The result is an image that is visually engaging, but that also allows viewers to rapidly and accurately explore and analyze the underlying data values. We conclude by applying our technique to a collection of environmental and weather readings, to demonstrate its viability in a practical, real-world visualization environment. 1

People often need to quickly access or maintain awareness of secondary information while busy with other primary tasks. Information visualizations provide rapid, effective access to information, but are generally designed to be examined by users as the primary focus of their attention. The goal of this research is to discover how to design information visualizations intended for the periphery and to understand how quickly and effectively people can interpret information visualizations while they are busily performing other tasks. We evaluated how several factors of a visualization (visual density, presence time, and secondary task type) impact people's abilities to continue with a primary task and to complete secondary tasks related to the visualization. Our results suggest that, with relaxed time pressure, reduced visual information density and a single well-defined secondary task, people can effectively interpret visualizations with minimal distraction to their primary task.

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 article provides a snapshot of immersive virtual reality(IVR) use for scientific visualization, in the context of the evolution of computing in general and of user interfaces in particular. The main thesis of this article is that IVR has great potential for dealing with the serious problem of exponentiallygrowing scientific datasets. Our abilityto produce large datasets both through numerical simulation and through data acquisition via sensors is outrunning our abilityto make sense of those datasets. While our idea of "large" datasets used to be measured in hundreds of gigabytes, based at least in part on what we could easilystore, manipulate, and displayin real time, today's science and engineering are producing terabytes and soon even petabytes, both from observation via sensors and as output from numerical simulation. Clearly, visualization by itself will not solve the problem of understanding truly large datasets that would overwhelm both displaycapacityand the human visual system. We advocate a human--computer partnership that draws on the strengths of each partner, with algorithmic culling and feature-detection used to identifythe small fraction of the data that should be visuallyexamined in detail bythe human. Our hope is that IVR will be a potent tool to let humans "see" patterns, trends, and anomalies in their data well beyond what they can do with conventional 3D desktop displays. r 2002 Published b Elsevier Science Ltd.

Information Visualization (InfoVis) encompasses techniques of visualization that deal primarily with abstract data, that is, data for which the user has no preconceived mental model. This is in contrast to, for example, volume or flow data.