Visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data. Visualization exploration is more than presentation; the interaction with both the data and its depiction is as important as the data and depiction itself. Previous visualization research has focused on the generation of visualizations---the depiction---and not on the exploratory aspects of the visualization process. However, without user interfaces for and formal models of the visualization process, visualization exploration sessions cannot be fully utilized. Towards this end, this dissertation introduces a model and framework for the visualization exploration process. This research aims at providing a framework for capturing, representing, and manipulating information derived during the visualization discovery processes in a systematic manner. In particular, this work focuses on the exploration during the data analysis and visualization process through the use of intuitive graphical user interfaces. These interfaces provide a structured environment for the exploration of the visualization parameter space. The interfaces utilize a formal model of the visualization process that captures the fundamental operations performed during this exploration. The model is independent of the visualization performed or user interface utilized. In addition, instances of the model can be shared between users via an interoperable representation format. The goal of this work is to maximize user productivity by offering them an effective mechanism to fetch, edit, reuse, and share with others their visualizations which may include raw data, data associations, visualization results, and the steps taken to derive the visualization results.

The exploration of complex data sets requires interfaces to present and navigate through the visualization of the data. This abstract describes such an interface.

The purpose of the visualization process is to extract insight from information via visual representations. However, the visualization process itself is information and can be in turn visualized. This work describes a method for visualizing the visualization process using a new focus+context radial graph algorithm. Four different metrics for extracting information from the visualization are examined. Using an example from a network visualization system, the effectiveness of the visualization and metrics are demonstrated. Using the methods discussed here, a better understanding of a particular visualization process---and its corresponding results---is facilitated.

Exploring complex, very large data sets requires interfaces to present and navigate through the visualization of the data. Two types of audience benefit from such coherent organization and representation: first, the user of the visualization system can examine and evaluate their data more efficiently; second, collaborators or reviewers can quickly understand and extend the visualization. The needs of these two groups are addressed by the spreadsheet-like interface described here. The interface represents a 2-dimensional window into a multi-dimensional visualization parameter space. Data is explored by navigating this space via the interface. The visualization space is presented to the user in a manner that clearly identifies which parameters correspond to which visualized result. Operations defined on this space can be applied which generate new parameters or results. Combined with a general purpose interpreter, these functions can be utilized to quickly extract desired results. Finally, by encapsulating the visualization process, redundant exploration is eliminated and collaboration is facilitated. The efficacy of this novel interface is demonstrated through examples using a variety of data sets in different domains

The current state of the art in visualization research places a strong emphasis on different techniques to derive insight from disparate types of data. However, little work has investigated the visualization process itself. The information content of the visualization process---the results, history, and relationships between those results---is addressed by this work. A characterization of the visualization process is discussed, leading to a general model of the visualization exploration process. The model, based upon a new parameter derivation calculus, can be used for automated reporting, analysis, or visualized directly. An XML-based language for expressing visualization sessions using the model is also described. These sessions can then be shared and reused by collaborators. The model, along with the XML representation, provides an effective means to utilize the information within the visualization process to further data exploration.

As the size and complexity of data sets continues to increase, the development of user interfaces and interaction techniques that expedite the process of exploring that data must receive new attention. Regardless of the speed of rendering, it is important to coherently organize the visual process of exploration: this information both grants insights about the data to a user and can be used by collaborators to understand the results. To fulfill these needs, we present a spreadsheet-like interface to data exploration. The interface displays a 2-dimensional window into visualization parameter space which users manipulate as they search for desired results. Through tabular organization and a clear correspondence between parameters and results, the interface eases the discovery, comparison and analysis of the underlying data. Users can utilize operators and the integrated interpreter to further explore and automate the visualization process; using a method introduced in this paper, these operations can be applied to ``cells'' in different ``stacks'' of the interface. Via illustrations using a variety of data sets, we demonstrate the efficacy of this novel interface.

To ensure the normal operation of a large computer network system, the common practice is to constantly collect system logs and analyze the network activities for detecting anomalies. Most of the analysis methods in use today are highly automated due to the enormous size of the collected data. Conventional automated methods are largely based on statistical modeling, and some employ machine learning. In this paper, we show interactive visualization as an alternative and effective data exploration method for understanding the complex behaviors of computer network systems. We describe three log-file analysis applications, and demonstrate how the use of our visualization-centered tools can lead to the discovery of flaws and intruders in the network systems.

The proper usage and creation of transfer functions for time-varying data sets is an often ignored problem in volume visualization. Although methods and guidelines exist for time-invariant data, little formal study for the time-varying case has been performed. This paper examines this problem, and reports the study that we have conducted to determine how the dynamic behavior of time-varying data may be captured by a single or small set of transfer functions. The criteria which dictate when more than one transfer function is needed were also investigated. Four data sets with different temporal characteristics were used for our study. Results obtained using two different classes of methods are discussed, along with lessons learned. These methods, including a new multi-resolution opacity map approach, can be used for semi-automatic generation of transfer functions to explore large-scale time-varying data sets.

Graph and tree visualization techniques enable interactive exploration of complex relations while communicating topology. However, most existing techniques have not been designed for situations where visual information such as images is also present at each node and must be displayed. This paper presents MoireGraphs to address this need. MoireGraphs combine a new focus+context radial graph layout with a suite of interaction techniques (focus strength changing, radial rotation, level highlighting, secondary foci, animated transitions and node information) to assist in the exploration of graphs with visual nodes. The method is scalable to hundreds of displayed visual nodes.

Image resectioning is the process of taking 2D images of a 3D model and mapping those images back onto the model so the images can be viewed in 3-space. Consider photographic images of oil-slick tests on an airplane wing; image resectioning allows a scientist to take the image of the wing, shot at different locations and orientations, and visualize them as a continuous, 3-dimensional model. We aim to develop algorithms to automate the image resectioning problem with as little human interaction as possible. This abstract describes one of the steps toward that goal- finding points in the image and model that we can use for matching.