Dynamic Fisheye Views: Combining Dynamic Queries and Mapping with Database View Definition

Dynamic Fisheye Views: Combining Dynamic Queries and Mapping with Database View Definition Doctor of Philosophy, 1996 Department of Computer Science University of Toronto Information (or data) visualization refers to the graphical presentation of information with the aim of providing the viewer with a qualitative understanding of the information contents. Two common graphical methods for depicting multidimensional data are scatterplots (that show relationships among data dimensions) and node-andlink diagrams or graphs (that show relationships among individual data points). One strategy to aid understanding of multidimensional data is to map data dimensions or attributes (e.g., temperature, population, location) to graphic properties (e.g., colour, size, position). Dynamic mapping (DM) is an interactive technique that permits mappings to be adjusted dynamically. The dynamic query (DQ) is a related interactive technique that applies the principles of direct manipulation to the searching of multidimensional data: visual presentation of query’s components (e.g., sliders, radio buttons); visual presentation of results; rapid, incremental, and reversible control of queries; selection by pointing, not typing; and immediate and continuous feedback. User experiments that compared DQ to more traditional formfill-in or textual query approaches, have shown that DQ interfaces are superior for many searching tasks. DQ and DM are complementary techniques and have been successfully integrated in a number of recent visualization systems. However, both techniques share a common limitation: their effectiveness is inseparably tied to the availability of suitable multidimensional data (to query and map). A standard way to generate additional derived attributes in database systems is to define views over existing base data. In addition, while existing DQ systems are well-suited for tasks that involve identifying data points that satisfy selection criteria, they offer little or no help in solving tasks that involve identifying data points that are “similar” to one or more selected data points. This second limitation of existing DQ systems stems from the fact that they do not incorporate similarity (or distance) metrics. The absence of similarity metrics means that many conceptually simple searching tasks can require substantial cognitive effort. For example, consider tasks such as: find flight destinations that can be reached inexpensively or quickly from one or more departing cities, or, find movies that i are similar to “Star Wars” (e.g., incorporating some combination of plot, actors, director, year, style, etc.). A set of techniques that do employ similarity or distance metrics are a variety of fisheye views (FEVs). In FEVs, similarity or distance metrics (between any pair of data points), are used to assign to each point in the visualization a number that reflects the user’s degree of interest in that point relative to one or more user-selected (focal) points. Most FEV techniques can be divided into two categories: filtering FEVs that simplify large visualizations by hiding “uninteresting” points; and distorting FEVs that distort the sizes and positions of points in the visualization in an attempt to smoothly integrate local detailed views with global contextual views. As such, existing FEV techniques are subject to two key limitations: specialized hard-coded notions of similarity, and static and limited presentation strategies. In this thesis we describe a novel approach that simultaneously simplifies and generalizes the notion of FEV and extends the power of DQ. In particular, we propose a view definition language, Dye, based on an objectoriented algebra with object-preserving semantics, and demonstrate how this language can be used to define and to efficiently evaluate a class of derived attributes that capture notions of similarity. Next, we show that incorporating such views with DQ and DM: extends the power of DQ by allowing similarity queries to be posed by pointing and clicking, thereby converting previously challenging cognitive tasks into simple perceptual tasks (e.g., recognizing sets of red points); and, simplifies and generalizes the notion of FEV; with this approach filtering FEVs are created by applying DQ to similarity attributes; distorting FEVs are created by mapping similarity attributes to position and size properties with DM; and more general types of FEVs are created by mapping similarity attributes to other graphic properties (e.g., the hue and saturation components of colour). As proof of concept, we have implemented an extensive graph visualization system (Graphite) that combines DQ, DM, flexible automatic graph layout, and an active database view component that implements Dye. In this thesis, we demonstrate how Graphite is used to pose similarity queries and to generate a variety of FEVs through several representative sample applications: transportation networks (airline flights data); abstract networks (an active World-Wide Web structure visualization that enhances existing Web browsers); biological data (genome structure graph visualization); geographic information systems (U.S. cities); and, hierarchical data visualization (Unix file directories).