In the last several years, large multi-dimensional databases have become common in a variety of applications such as data warehousing and scientific computing. Analysis and exploration tasks place significant demands on the interfaces to these databases. Because of the size of the data sets, dense graphical representations are more effective for exploration than spreadsheets and charts. Furthermore, because of the exploratory nature of the analysis, it must be possible for the analysts to change visualizations rapidly as they pursue a cycle involving first hypothesis and then experimentation.

Multiple coordinated visualizations enable users to rapidly explore complex information. However, users often need unforeseen combinations of coordinated visualizations that are appropriate for their data. Snap-Together Visualization enables data users to rapidly and dynamically mix and match visualizations and coordinations to construct custom exploration interfaces without programming. Snap's conceptual model is based on the relational database model. Users load relations into visualizations then coordinate them based on the relational joins between them. Users can create different types of coordinations such as: brushing, drill down, overview and detail view, and synchronized scrolling. Visualization developers can make their independent visualizations snap-able with a simple API. Evaluation of Snap revealed benefits, cognitive issues, and usability concerns. Data savvy users were very capable and thrilled to rapidly construct powerful coordinated visualizations. A snapped overview...

As graph models are applied to more widely varying fields, researchers struggle with tools for exploring and analyzing these structures. We describe GUESS, a novel system for graph exploration that combines an interpreted language with a graphical front end that allows researchers to rapidly prototype and deploy new visualizations. GUESS also contains a novel, interactive interpreter that connects the language and interface in a way that facilities exploratory visualization tasks. Our language, Gython, is a domain-specific embedded language which provides all the advantages of Python with new, graph specific operators, primitives, and shortcuts. We highlight key aspects of the system in the context of a large user survey and specific, real-world, case studies ranging from social and knowledge networks to distributed computer network analysis.

This paper presents the Snap user interface and basic conceptual model and then reports on two studies on constructing and operating coordinated visualizations

As computational power and storage capacity increase, processing and analyzing large volumes of multi-dimensional datasets play an increasingly important part in many domains of scientific research. Our study of a large set of scientific applications over the past three years indicates that the processing for such datasets is often highly stylized and shares several important characteristics. Usually, both the input dataset as well as the result being computed have underlying multi-dimensional grids. The basic processing step usually consists of transforming individual input items, mapping the transformed items to the output grid and computing output items by aggregating, in some way, all the transformed input items mapped to the corresponding grid point. In this paper, we present the design of T2, a customizable parallel database that integrates storage, retrieval and processing of multi-dimensional datasets. T2 provides support for common operations including index generation, data r...

In current windowing environments, individual windows are treated independently, making it difficult for users to coordinate information across multiple windows. While coordinated multi-window strategies are increasingly used in visualization and web user interfaces, designs are inflexible and haphazard. The space of such linkedwindow strategies is not well understood and largely unexplored. This paper presents a taxonomy of coordinations, identifies important components, and reviews example interfaces. This 2x3 taxonomy provides guidelines for designers of applications, user interface toolkits, and window managers. We hope to encourage construction of generalized, end-user programmable, robust, multiple-window coordination capabilities. KEYWORDS User Interface, Coordination, Taxonomy, Multiple Window Strategies, Information Visualization,Window Management INTRODUCTION Users are dealing with increasing quantity, variety, and interrelated-ness of information. User tasks are becoming...

Rivet is a visualization system for the study of complex computer systems. Since computer systems analysis and visualization is an unpredictable and iterative process, a key design goal of Rivet is to support the rapid development of interactive visualizations capable of visualizing large data sets. In this paper, we present Rivet's architecture, focusing on its support for varied data sources, interactivity, composition and user-defined data transformations. We also describe the challenges of implementing this architecture efficiently and flexibly. We conclude with several examples of computer systems visualizations generated within Rivet, including studies of parallel systems, superscalar processors, and mobile network usage. 1

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Applications that make use of very large scientific datasets have become an increasingly important subset of scientific applications. In these applications, datasets are often multi-dimensional, i.e., data items are associated with points in a multi-dimensional attribute space, and access to data items is described by range queries. The basic processing involves mapping input data items to output data items, and some form of aggregation of all the input data items that project to the each output data item. We have developed an infrastructure, called the Active Data Repository (ADR), that integrates storage, retrieval and processing of multi-dimensional datasets on distributed-memory parallel architectures with multiple disks attached to each node. In this paper we address efficient execution of range queries on distributed memory parallel machines within ADR framework. We present three potential strategies, and evaluate them under different application scenarios and machine co...

As computational power and storage capacity increase, processing and analyzing large volumes of multi-dimensional datasets play an increasingly important part in many domains of scientific research. Several database research groups and vendors have developed object-relational database systems to provide some support for managing and/or visualizing multi-dimensional datasets. These systems, however, provide little or no support for analyzing or processing these datasets -- the assumption is that this is too application-specific to warrant common support. As a result, applications that process these datasets are usually decoupled from data storage and management, resulting in inefficiency due to copying and loss of locality. Furthermore, every application developer has to implement complex support for managing and scheduling the processing. Our study of a large set of scientific applications over the past three years indicates that the processing for such datasets is often highly stylize...