Melanie Tory

Collaborative coupling over tabletop displays

Designing collaborative interfaces for tabletops remains difficult because we do not fully understand how groups coordinate their actions when working collaboratively over tables. We present two observational studies of pairs completing independent and shared tasks that investigate collaborative coupling, or the manner in which collaborators are involved and occupied with each others work. Our results indicate that individuals frequently and fluidly engage and disengage with group activity through several distinct, recognizable states with unique characteristics. We describe these states and explore the consequences of these states for tabletop interface design.

Evaluating visualizations: do expert reviews work?

Visualization research generates beautiful images and impressive interactive systems. Emphasis on evaluating visualizations is growing. Researchers have successfully used alternative evaluation techniques in human-computer interaction (HCI), including focus groups, field studies, and expert reviews. These methods tend to produce qualitative results and require fewer participants than controlled experiments. In this article, we focus on expert reviews that we used for the applications. We commonly use expert reviews to assess interface usability. Expert reviews can generate valuable feedback on visualization tools. We recommend i) including experts with experience in data display as well as usability, and ii) developing heuristics based on visualization guidelines as well as usability guidelines. Expert reviews should not be used exclusively, since experts might not hilly predict end-user actions. Furthermore, we encourage more experimentation with this technique, particularly to develop a good set of visualization heuristics and to compare it with other methods.

How Information Visualization Novices Construct Visualizations

It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.

Visualization task performance with 2D, 3D, and combination displays

We describe a series of experiments that compare 2D displays, 3D displays, and combined 2D/3D displays (orientation icon, ExoVis, and clip planes) for relative position estimation, orientation, and volume of interest tasks. Our results indicate that 3D displays can be very effective for approximate navigation and relative positioning when appropriate cues, such as shadows, are present. However, 3D displays are not effective for precise navigation and positioning except possibly in specific circumstances, for instance, when good viewing angles or measurement tools are available. For precise tasks in other situations, orientation icon and ExoVis displays were better than strict 2D or 3D displays (displays consisting exclusively of 2D or 3D views). The combined displays had as good or better performance, inspired higher confidence, and allowed natural, integrated navigation. Clip plane displays were not effective for 3D orientation because users could not easily view more than one 2D slice at a time and had to frequently change the visibility of individual slices. Major factors contributing to display preference and usability were task characteristics, orientation cues, occlusion, and spatial proximity of views that were used together.

A parallel coordinates style interface for exploratory volume visualization

We present a user interface, based on parallel coordinates, that facilitates exploration of volume data. By explicitly representing the visualization parameter space, the interface provides an overview of rendering options and enables users to easily explore different parameters. Rendered images are stored in an integrated history bar that facilitates backtracking to previous visualization options. Initial usability testing showed clear agreement between users and experts of various backgrounds (usability, graphic design, volume visualization, and medical physics) that the proposed user interface is a valuable data exploration tool.

Personal Visualization and Personal Visual Analytics

Data surrounds each and every one of us in our daily lives, ranging from exercise logs, to archives of our interactions with others on social media, to online resources pertaining to our hobbies. There is enormous potential for us to use these data to understand ourselves better and make positive changes in our lives. Visualization (Vis) and visual analytics (VA) offer substantial opportunities to help individuals gain insights about themselves, their communities and their interests; however, designing tools to support data analysis in non-professional life brings a unique set of research and design challenges. We investigate the requirements and research directions required to take full advantage of Vis and VA in a personal context. We develop a taxonomy of design dimensions to provide a coherent vocabulary for discussing personal visualization and personal visual analytics. By identifying and exploring clusters in the design space, we discuss challenges and share perspectives on future research. This work brings together research that was previously scattered across disciplines. Our goal is to call research attention to this space and engage researchers to explore the enabling techniques and technology that will support people to better understand data relevant to their personal lives, interests, and needs.

A Taxonomy of Visual Cluster Separation Factors

We provide two contributions, a taxonomy of visual cluster separation factors in scatterplots, and an in‐depth qualitative evaluation of two recently proposed and validated separation measures. We initially intended to use these measures to provide guidance for the use of dimension reduction (DR) techniques and visual encoding (VE) choices, but found that they failed to produce reliable results. To understand why, we conducted a systematic qualitative data study covering a broad collection of 75 real and synthetic high‐dimensional datasets, four DR techniques, and three scatterplot‐based visual encodings. Two authors visually inspected over 800 plots to determine whether or not the measures created plausible results. We found that they failed in over half the cases overall, and in over two‐thirds of the cases involving real datasets. Using open and axial coding of failure reasons and separability characteristics, we generated a taxonomy of visual cluster separability factors. We iteratively refined its explanatory clarity and power by mapping the studied datasets and success and failure ranges of the measures onto the factor axes. Our taxonomy has four categories, ordered by their ability to influence successors: Scale, Point Distance, Shape, and Position. Each category is split into Within‐Cluster factors such as density, curvature, isotropy, and clumpiness, and Between‐Cluster factors that arise from the variance of these properties, culminating in the overarching factor of class separation. The resulting taxonomy can be used to guide the design and the evaluation of cluster separation measures.

DimStiller: Workflows for dimensional analysis and reduction

DimStiller is a system for dimensionality reduction and analysis. It frames the task of understanding and transforming input dimensions as a series of analysis steps where users transform data tables by chaining together different techniques, called operators, into pipelines of expressions. The individual operators have controls and views that are linked together based on the structure of the expression. Users interact with the operator controls to tune parameter choices, with immediate visual feedback guiding the exploration of local neighborhoods of the space of possible data tables. DimStiller also provides global guidance for navigating data-table space through expression templates called workflows, which permit re-use of common patterns of analysis.

Music selection using the PartyVote democratic jukebox

PartyVote is a democratic music jukebox designed to give all participants an equal influence on the music played at social gatherings or parties. PartyVote is designed to provide appropriate music in established social groups with minimal user interventions and no pre-existing user profiles. The visualization uses dimensionality reduction to show song similarity and overlays information about how votes affect the music played. Visualizing voting decisions allows users to link music selections with individuals, providing social awareness. Traditional group norms can subsequently be leveraged to maintain fair system use and empower users.

eSeeTrack—Visualizing Sequential Fixation Patterns

We introduce eSeeTrack, an eye-tracking visualization prototype that facilitates exploration and comparison of sequential gaze orderings in a static or a dynamic scene. It extends current eye-tracking data visualizations by extracting patterns of sequential gaze orderings, displaying these patterns in a way that does not depend on the number of fixations on a scene, and enabling users to compare patterns from two or more sets of eye-gaze data. Extracting such patterns was very difficult with previous visualization techniques. eSeeTrack combines a timeline and a tree-structured visual representation to embody three aspects of eye-tracking data that users are interested in: duration, frequency and orderings of fixations. We demonstrate the usefulness of eSeeTrack via two case studies on surgical simulation and retail store chain data. We found that eSeeTrack allows ordering of fixations to be rapidly queried, explored and compared. Furthermore, our tool provides an effective and efficient mechanism to determine pattern outliers. This approach can be effective for behavior analysis in a variety of domains that are described at the end of this paper.