Matthew Brehmer

A Multi-Level Typology of Abstract Visualization Tasks

The considerable previous work characterizing visualization usage has focused on low-level tasks or interactions and high-level tasks, leaving a gap between them that is not addressed. This gap leads to a lack of distinction between the ends and means of a task, limiting the potential for rigorous analysis. We contribute a multi-level typology of visualization tasks to address this gap, distinguishing why and how a visualization task is performed, as well as what the task inputs and outputs are. Our typology allows complex tasks to be expressed as sequences of interdependent simpler tasks, resulting in concise and flexible descriptions for tasks of varying complexity and scope. It provides abstract rather than domain-specific descriptions of tasks, so that useful comparisons can be made between visualization systems targeted at different application domains. This descriptive power supports a level of analysis required for the generation of new designs, by guiding the translation of domain-specific problems into abstract tasks, and for the qualitative evaluation of visualization usage. We demonstrate the benefits of our approach in a detailed case study, comparing task descriptions from our typology to those derived from related work. We also discuss the similarities and differences between our typology and over two dozen extant classification systems and theoretical frameworks from the literatures of visualization, human-computer interaction, information retrieval, communications, and cartography.

Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists.

For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview, an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system “in the wild”, and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of “exploring” a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overviews design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology.

Visualizing dimensionally-reduced data: interviews with analysts and a characterization of task sequences

We characterize five task sequences related to visualizing dimensionally-reduced data, drawing from data collected from interviews with ten data analysts spanning six application domains, and from our understanding of the technique literature. Our characterization of visualization task sequences for dimensionally-reduced data fills a gap created by the abundance of proposed techniques and tools that combine high-dimensional data analysis, dimensionality reduction, and visualization, and is intended to be used in the design and evaluation of future techniques and tools. We discuss implications for the evaluation of existing work practices, for the design of controlled experiments, and for the analysis of post-deployment field observations.

Classifying input for active games

Active games are video games that involve physical activity. Active games capture input via a variety of devices such as accelerometers, cameras, pressure sensors and exercise equipment. Although active games have become highly popular, the interaction styles they support are poorly understood, and largely driven by the capabilities of individual hardware devices. In order to allow for a standard development approach, a better understanding of the interaction found in active games is required. We have investigated existing commercial and academic games in order to classify input for active games. Our classification abstracts input from hardware, providing a better understanding of the interaction itself. Our ultimate goal is to make it easier to develop active games independently of underlying input hardware.

Matches, Mismatches, and Methods: Multiple-View Workflows for Energy Portfolio Analysis

The energy performance of large building portfolios is challenging to analyze and monitor, as current analysis tools are not scalable or they present derived and aggregated data at too coarse of a level. We conducted a visualization design study, beginning with a thorough work domain analysis and a characterization of data and task abstractions. We describe generalizable visual encoding design choices for time-oriented data framed in terms of matches and mismatches, as well as considerations for workflow design. Our designs address several research questions pertaining to scalability, view coordination, and the inappropriateness of line charts for derived and aggregated data due to a combination of data semantics and domain convention. We also present guidelines relating to familiarity and trust, as well as methodological considerations for visualization design studies. Our designs were adopted by our collaborators and incorporated into the design of an energy analysis software application that will be deployed to tens of thousands of energy workers in their client base.

Pre-design empiricism for information visualization: scenarios, methods, and challenges

Empirical study can inform visualization design, both directly and indirectly. Pre-design empirical methods can be used to characterize work practices and their associated problems in a specific domain, directly motivating design choices during the subsequent development of a specific application or technique. They can also be used to understand how individuals, existing tools, data, and contextual factors interact, indirectly informing later research in our community. Contexts for empirical study vary and practitioners should carefully consider finding the most appropriate methods for any given situation. This paper discusses some of the challenges associated with conducting pre-design studies by way of four illustrative scenarios, highlighting the methods as well as the challenges unique to the visualization domain. We encourage researchers and practitioners to conduct more pre-design empirical studies and describe in greater detail their use of empirical methods for informing design.

Activate your GAIM: a toolkit for input in active games

Active games are video games that involve physical activity. Interaction in active games is captured via a variety of input devices such as accelerometers, cameras, pressure sensors and exercise equipment. It is difficult for programmers to deal with this profusion of devices, leading most active games to be tied to a particular hardware platform. In this paper, we introduce the GAIM toolkit. GAIM simplifies input handling in active games through a high-level API that abstracts the details of individual devices. This allows developers to write code independently of the input devices used, allows the toolkit to dynamically adapt to the devices a player has available, and allows people with different hardware to play together. We illustrate the approach through two active games developed using the GAIM toolkit.

The haptic crayola effect: Exploring the role of naming in learning haptic stimuli

A haptic icon is a short physical stimulus attached to a simple meaning, which provides information and feedback to a user. To scale the utility demonstrated for small icon sets to larger ones, we need efficient strategies to help users learn subtle distinctions among stimuli, in a modality for which they may not hold detailed descriptive percepts. This paper investigates the effect of naming haptic stimuli - i.e. explicitly creating a linguistic marker - on the accuracy with which users are able to identify, distinguish, and recall stimuli. We conducted a between-subjects experiment using 60 participants equally divided among three naming conditions: no names, pre-selected non-descriptive names, and self-selected names. The experiment examined the impact of naming strategy on the ability of participants to identify stimuli in a nonverbal matching test, and on remembering stimulus names. For this challenging task and the degree of learning afforded, naming did not significantly impact accuracy of matching stimuli to meanings for all participants. However, more than twice of many of those allowed to choose names reported the ability to remember and distinguish stimuli than those required to use non-descriptive names, and many participants felt that the names were useful. Of middle-performing participants, the self-selected names group performed significantly better than the non-descriptive names group, and appeared to progress more quickly in learning. We summarize evidence for a trend that might widen with refined naming strategies and more extensive learning.

C-TOC (Cognitive Testing on Computer): investigating the usability and validity of a novel self-administered cognitive assessment tool in aging and early dementia.

Introduction:Cognitive Testing on Computer (C-TOC) is a novel computer-based test battery developed to improve both usability and validity in the computerized assessment of cognitive function in older adults. Methods:C-TOC’s usability was evaluated concurrently with its iterative development to version 4 in subjects with and without cognitive impairment, and health professional advisors representing different ethnocultural groups. C-TOC version 4 was then validated against neuropsychological tests (NPTs), and by comparing performance scores of subjects with normal cognition, Cognitive Impairment Not Dementia (CIND) and Alzheimer disease. C-TOC’s language tests were validated in subjects with aphasic disorders. Results:The most important usability issue that emerged from consultations with 27 older adults and with 8 cultural advisors was the test-takers’ understanding of the task, particularly executive function tasks. User interface features did not pose significant problems. C-TOC version 4 tests correlated with comparator NPT (r=0.4 to 0.7). C-TOC test scores were normal (n=16)>CIND (n=16)>Alzheimer disease (n=6). All normal/CIND NPT performance differences were detected on C-TOC. Low computer knowledge adversely affected test performance, particularly in CIND. C-TOC detected impairments in aphasic disorders (n=11). Discussion:In general, C-TOC had good validity in detecting cognitive impairment. Ensuring test-takers’ understanding of the tasks, and considering their computer knowledge appear important steps towards C-TOC’s implementation.

Usability and the effects of interruption in C-TOC : self-administered cognitive testing on a computer

 mabrehme@microsoft.com  mattbrehmer.github.io  aka.ms/mabrehme  twitter/mattbrehmer  linkedin/matthewbrehmer  skype/matthewbrehmer  github/mattbrehmer  bl.ocks/mattbrehmer I am a postdoctoral researcher at Microsoft Research in Redmond, Washington, where I specialize in expressive information visualization design and data visualization for mobile devices. I am a member of the EPIC (Extended Perception, Interaction & Cognition) and Human Computer Interaction groups, and I am a liated with Microsofts Data Journalism Program.