An Investigation of Issues and Techniques in Highly Interactive Computational Visualization by Michael John MCGuffin A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Computer Science University of Toronto Copyright c 2007 by Michael John MCGuffin Abstract An Investigation of Issues and Techniques in Highly Interactive Computational Visualization Michael John MCGuffin Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2007 Computers have changed the nature of data visualization in two important ways. First, computers allow increasingly large data sets to be automatically processed. Thus, much research effort has focused on increasing the speed and quality of graphical rendering, to accommodate data sets having greater cardinality, resolution, number of dimensions, etc. Second, computers enable interactive exploration of different views and depictions of the same data over time. Although interactive visualization is now a norm, we argue that strategies for easing and improving this interaction are relatively underexploited, and show this in three case studies. Each study involves the design and implementation of novel techniques to aid visualization in some application domain. We assert the usefulness of a small set of guiding design goals for interactive visualization that are: to ease input, to augment output over space by increasing and improving output in any given state, and to augment output over time by using smooth transitions between states. The utility of these goals is demonstrated, firstly, through the results of each case study driven in part by the goals, and secondly, by using the experience gained from the case studies to generate a set of concrete design guidelines that specify how to better achieve the design goals, and that can be applied in future design work. The first study considers visualization of 3D volumetric data, and uses spatial defor- ii mations to reduce occlusion and increase the visibility of internal surfaces of the data. The second involves visualizing and browsing a rooted tree, and uses a space-filling al- gorithm to automatically increase the number of nodes visible to the user. The third examines visualization of genealogical graphs, and develops layout techniques to avoid crowding of nodes and edge crossings. Each of the three studies pays attention to the meaningful subsets that exist within the data, and how to exploit these subsets during interaction. Each study also introduces an interaction technique enabling rapid and light-weight traversal of entire sequences of states, with visually continuous transitions across states. Following the case studies, the issues encountered are analyzed, a taxonomy of parameter manipulation is developed, and guidelines for future design are generated to help achieve the original design goals. Taken individually, each case study contributes significantly to its domain in the specific aspects it investigates. Furthermore, the relative variety in our case studies results in a broad perspective with which to analyze the issues encountered. The unified contribution of the work lies in demonstrating multiple ways of pursuing the design goals, and in the design guidelines subsequently generated that can be applied to situations beyond our cases studies. iii Acknowledgements My doctoral studies have been a rich experience, at times fun, easy, exciting, and ego-boosting, and at other times trying, humbling, sleep-depriving, and exhausting. The biggest rewards I’ve enjoyed so far from the experience have been the opportunity to publish research, and the researchers and friends I would not have met otherwise. I benefited immeasurably from having Ravin Balakrishnan as my supervisor. The amount of supervision and direction he gave me matched the amount I wanted: my frequent visits to his office early in my graduate studies were met with an open door, and my later decision to work more independently was encouraged with considerable freedom and trust from him. Much of his advice consisted of key tips or nuggets of insight, and in hindsight, almost every piece of advice he gave me was excellent and valuable. He also responded quickly to requests for help, e.g. to read over documents for comment. Ravin is a strong advocate for his students, and a motivating role model of efficiency, pragmatism, energy, and ambition. I have also benefited greatly from an environment of many other excellent researchers who have provided me with direction, ideas, and expert knowledge. These include the members of my doctoral committee: Ravin, Gordon Kurtenbach (my former manager at Alias|wavefront when I interned there as an undergraduate, and the person who convinced me to pursue graduate studies), Ronald M. Baecker (who was formerly my professor in a course on CSCW), and Karan Singh (who was formerly my professor in a course on animation) — in preparing this dissertation, my committee’s wisdom was to point me in a direction that I initially didn’t have much faith in, but that turned out to be more fruitful than I expected — ; my co-authors; faculty and grad students in the Dynamic Graphics Project lab (including Joe Laszlo, Gonzalo Ramos, Maciej Kalisiak, Michael Tsang, G´ery Casiez, Pierre Dragicevic, Anand Agarawala, Glenn Tsang, Michael Neff, and many others), Mark H. Chignell’s Interactive Media Lab (including Shengdong Zhao and Sandra Jean “Sacha” “pimp my emacs” “it’s insanely programmable” “I have too iv much fun talking about emacs” Chua) and Ian Spence’s Engineering Psychology Lab; the Department of Computer Science at University of Toronto (including Bowen Hui); and fellow researchers met at conferences. Special thanks are due to my external examiner, Sheelagh Carpendale, whose ex- pertise made her reading of the dissertation a valuable and much appreciated check in quality. Part of my doctoral research was funded, facilitated, and stimulated by time spent at the IBM Toronto Laboratory, where I benefited from exchanges with Gord Davison, Rebecca Wong, David Budreau, Paul W. Smith, and Jen Hawkins. G´ery Casiez and Pierre Dragicevic helped me hone my ability to discuss research in French, as did the FSL2 (“Frites, salade, les deux?”) lunch meetings (in Pierre’s words: “Luttons pour l’´egalit´e, la paix et la pr´esence de frites et salade `aparts ´egales dans nos assiettes”). Finally, thanks to the management and wait staff at 168 Bubble Tea Shop #36 (my green-tinted “other office”), especially Vivim; friends who kept me intellectually stimulated (including Eugene Kim and Brian Wong), and thanks most of all to Alicia, my children, and our extended families, for moral support, giving me time to work, and putting up with my frequent blank stares when my mind was on research. v Contents 1 Introduction 1 2 Background 5 2.1 OverviewofVisualization .......................... 5 2.1.1 Visualization of Multi-dimensional Multi-variate (mdmv) Data . 7 2.1.2 VisualizationofGraphs ....................... 9 2.1.3 OtherDataTypes .......................... 12 2.1.4 Scientific Visualization versus Information Visualization . 13 2.2 Design Goals for Highly Interactive Visualization . ..... 14 2.2.1 IncreasingandImprovingOutput . 16 2.2.2 EasingInput ............................. 18 2.2.3 Using Smooth Transitions Between States . 19 2.2.4 ClosingRemarks ........................... 21 3 Using Deformations for Browsing Volumetric Data 22 3.1 Introduction.................................. 23 3.1.1 Approach to Pursuing Design Goals . 25 3.2 Background .................................. 26 3.2.1 VolumetricData ........................... 26 3.2.2 RelatedWork ............................. 28 3.3 DesignApproach ............................... 30 vi 3.4 PrototypeImplementation . 34 3.4.1 HingeSpreaderTool ......................... 41 3.4.2 SphereExpanderTool . 43 3.4.3 BoxSpreaderTool .......................... 44 3.4.4 LeaferTool .............................. 45 3.4.5 PeelerTool .............................. 46 3.4.6 Radial Peeler Tool . 47 3.4.7 Observations ............................. 48 3.4.8 InitialUserFeedback . 50 3.5 ClosingRemarks ............................... 52 4 Expand-Ahead: A Space-Filling Strategy for Trees 54 4.1 Introduction.................................. 55 4.1.1 Approach to Pursuing Design Goals . 57 4.2 Background .................................. 59 4.3 TheExpand-AheadAlgorithm. 61 4.4 ComparisonwithFurnas’DOI. 63 4.5 1DPrototype ................................. 65 4.5.1 A Rough Model of User Performance . 65 4.6 2DPrototype ................................. 69 4.7 ProsandConsofExpand-Ahead . 74 4.8 ControlledExperiment . 75 4.8.1 Goals.................................. 75 4.8.2 Apparatus............................... 75 4.8.3 Participants .............................. 76 4.8.4 Task .................................. 76 4.8.5 Conditions............................... 77 4.8.6 ResultsandDiscussion ........................ 79 vii 4.9 Design Issues and Potential Enhancements . 82 4.9.1 Sticky or Hard Expansion, versus Soft Expansion . 82 4.9.2 Locking Node Positions for Persistent Layout . 82 4.9.3 Uniform Expand-Ahead and Partial Expansion . 83 4.9.4 Improvements in Graphic Design . 83 4.10ClosingRemarks ............................... 83 5 Interactive Visualization of Genealogical Graphs 85 5.1 Introduction.................................. 85 5.1.1 Approach to Pursuing Design Goals . 88 5.2 Background .................................. 89 5.3 Analysis of Genealogical Graphs . 92 5.3.1 Preliminaries