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. CR Categories: H.5.2 [Information interfaces and presentation]: User interfaces—Graphical user interfaces, interaction styles and user-centered design

In this paper, we focus on some of the key design decisions we faced during the process of architecting a visualization system and present some possible choices, with their associated advantages and disadvantages. We frame this discussion within the context of Rivet, our general visualization environment designed for rapidly prototyping interactive, exploratory visualization tools for analysis. As we designed increasingly sophisticated visualizations, we needed to refine Rivet in order to be able to create these richer displays for larger and more complex data sets. The design

Abstract—Interaction cost is an important but poorly understood factor in visualization design. We propose a framework of interaction costs inspired by Norman’s Seven Stages of Action to facilitate study. From 484 papers, we collected 61 interaction-related usability problems reported in 32 user studies and placed them into our framework of seven costs: (1) Decision costs to form goals; (2) System-power costs to form system operations; (3) Multiple input mode costs to form physical sequences; (4) Physical-motion costs to execute sequences; (5) Visual-cluttering costs to perceive state; (6) View-change costs to interpret perception; (7) State-change costs to evaluate interpretation. We also suggested ways to narrow the gulfs of execution (2–4) and evaluation (5–7) based on collected reports. Our framework suggests a need to consider decision costs (1) as the gulf of goal formation.

Several information visualization techniques have been developed in the last few years due to the need of representing and analyzing the huge amount of data generated by several applications or made available through the World Wide Web. These techniques are usually interactive and thus provided as part of a graphical user interface. Evaluating user interfaces is accomplished to detect design problems in layout and interaction, while image quality is usually determined by visual inspection by experts in computer graphics. Information visualization techniques are usually reported showing their use in experimental situations, employing some kind of analysis.

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.

Visualization can be viewed as a process that transforms raw data (value) into views. There has been two major category of data process models that have been proposed to model the visualization transformation process. This paper seeks to compare the Data Flow Models and the Data State Models. Specifically, it proves that, in terms of expressiveness, anything that can represented using the Data Flow Model can also be represented using the Data State Model, and vice versa.

Abstract—To design information visualization tools that support users ’ needs, we need to understand how users engage with information visualizations in their analysis process. With the rapid growth in size and complexity of datasets, the practicality of an individual analyzing an entire dataset is becoming unrealistic. Instead, the expertise to make informed decisions about these information-rich datasets is often best accomplished by a team. However, there exist relatively few models that describe the visual analysis process, and only few studies that explore the differences between how individuals and teams use visualizations. We present an observational study where we explored the information analysis process of groups and individuals in the context of visual information. From the analysis of our study, we derive a framework that captures the activities of co-located teams and individuals engaged in information analysis. This framework has implications for the design, heuristic evaluation, and analysis of both collaborative and single-user digital information visualization tools.

Tabletop displays with touch-based input provide many powerful affordances for directly manipulating and collaborating around information visualizations. However, these devices also introduce several challenges for interaction designers, including discrepancies among the resolutions of the visualization, the tabletop’s display, and its sensing technologies; a need to support diverse types of interactions required by different visualization techniques; and the ability to support face-to-face collaboration. As a result, most interactive tabletop applications for working with information currently demonstrate limited functionality and do not approach the power or versatility of their desktop counterparts. We present a series of design considerations, informed by prior interaction design and focus+context visualization research, for ameliorating the challenges inherent in designing practical interaction techniques for tabletop information visualization applications. We then discuss two specific techniques, i-Loupe and iPodLoupe, which illustrate how different choices among these design considerations enable vastly different experiences in working with complex data on interactive surfaces. Author Keywords Resolution discrepancy, interaction lenses, information

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

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