ben @ cs.umd.edu A useful starting point for designing advanced graphical user interjaces is the Visual lnformation-Seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. This paper offers a task by data type taxonomy with seven data types (one-, two-, three-dimensional datu, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, Zoom, filter, details-on-demand, relate, history, and extracts). Everything points to the conclusion that the phrase 'the language of art ' is more

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section.

We describe the design, implementation, and pilot study for SenseMaker, an interface for information exploration across heterogeneous sources. We propose supporting the contextdriven evolution of a user's interests via: (1) an approximation of the current information context as the current collection of accumulated information references, and (2) a unified set of user-centered actions for examining the current context and for progressing from one context to the next. SenseMaker users examine their current context by experimenting iteratively with different organizing dimensions and levels of granularity for the current collection's display. They progress from one context to another by building upon, taking away from, or replacing the current collection. They can also return to a previous information context and continue exploring from there. Keywords Information exploration, digital libraries, information seeking, information retrieval INTRODUCTION The design of any interface must ...

Traditional database query systems allow users to construct complicated database queries from specialized database language primitives. While powerful and expressive, such systems are not easy to use, especially for browsing or exploring the data. Information visualization systems address this problem by providing graphical presentations of the data and direct manipulation tools for exploring the data. Recent work in this area has reported the value of dynamic queries coupled with two-dimensional data representations for progressive refinement of user queries. However, the queries generated by these systems are limited to conjunctions of global ranges of parameter values. In this paper, we extend dynamic queries by encoding each operand of the query as a Magic Lens filter. Compound queries can be constructed by overlapping the lenses. Each lens includes a slider and a set of buttons to control the value of the filter function and to define the compostion operation generated by overlapp...

FOCUS, the Feature-Oriented Catalog USer interface, is an interactive table viewer for a common kind of table, namely the object-attribute table, also called cases-by-attribute table or relational table. Typical examples of these tables are the Roll Calls in BYTE where the features and test results of a family of hardware or software products are compared. FOCUS supports data exploration by a combination of a focus+context or fisheye technique, a hierarchical outliner for large attribute sets, and a general and easy-to-use dynamic query mechanism where the user simply clicks on desired values found in the table. A PC/Windows implementation of FOCUS is publicly available

Interactive techniques are powerful tools for manipulating visualizations to analyze, communicate and acquire information. This is especially true for large data sets or complex 3D visualizations. Although many new types of interaction have been introduced recently, very little work has been done on understanding what their components are, how they are related and how they can be combined. This paper begins to address these issues with a framework for classifying interactive visualizations. Our goal is a framework that will enable us to develop toolkits for assembling visualization interfaces both interactively and automatically.

Exploratory data analysis (EDA) plays an increasingly important role in statistical analysis. EDA is difficult, however, even with the help of modern statistical software. We have developed an assistant for data exploration, based on AI planning techniques, that addresses some of the strategic shortcomings of conventional software. This paper illustrates the behavior of the system, gives a high level description of its design, and discusses its experimental evaluation.

In this paper, we discuss work aimed at the development of interactive decision-support tools for complex, large-scale scheduling applications. Our approach is grounded on three basic premises: (1) that system organization and decision-support "services" should directly reflect the inherently reactive nature of decision-making in complex scheduling environments, (2) that diversity in the character and requirements of various user tasks will invariably require different specialized scheduling services in different decision-making contexts, and (3) that problem scale and complexity will necessitate user interaction at aggregate, task-oriented levels. We describe DITOPS, a transportation scheduling tool which integrates a hierarchical modeling infra-structure and reactive scheduling methodology with graphical schedule visualization and manipulation capabilities to provide a flexible interactive environment for construction and management of transportation schedules. 1 Introduction Our sp...

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate repre- sentation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors.

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data has become increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques that have been developed over the last two decades to support the exploration of large data sets. In this star report, we provide an overview of information visualization and visual data mining techniques, and illustrate them using a few examples.