2,962 research outputs found
Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics
Clinicians and other analysts working with healthcare data are in need for
better support to cope with large and complex data. While an increasing number
of visual analytics environments integrates explicit domain knowledge as a
means to deliver a precise representation of the available data, theoretical
work so far has focused on the role of knowledge in the visual analytics
process. There has been little discussion about how such explicit domain
knowledge can be structured in a generalized framework. This paper collects
desiderata for such a structural framework, proposes how to address these
desiderata based on the model of linked data, and demonstrates the
applicability in a visual analytics environment for physiotherapy.Comment: 8 pages, 5 figure
Tangible user interfaces : past, present and future directions
In the last two decades, Tangible User Interfaces (TUIs) have emerged as a new interface type that interlinks the digital and physical worlds. Drawing upon users' knowledge and skills of interaction with the real non-digital world, TUIs show a potential to enhance the way in which people interact with and leverage digital information. However, TUI research is still in its infancy and extensive research is required in or- der to fully understand the implications of tangible user interfaces, to develop technologies that further bridge the digital and the physical, and to guide TUI design with empirical knowledge. This paper examines the existing body of work on Tangible User In- terfaces. We start by sketching the history of tangible user interfaces, examining the intellectual origins of this field. We then present TUIs in a broader context, survey application domains, and review frame- works and taxonomies. We also discuss conceptual foundations of TUIs including perspectives from cognitive sciences, phycology, and philoso- phy. Methods and technologies for designing, building, and evaluating TUIs are also addressed. Finally, we discuss the strengths and limita- tions of TUIs and chart directions for future research
Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems
Many interactive data systems combine visual representations of data with
embedded algorithmic support for automation and data exploration. To
effectively support transparent and explainable data systems, it is important
for researchers and designers to know how users understand the system. We
discuss the evaluation of users' mental models of system logic. Mental models
are challenging to capture and analyze. While common evaluation methods aim to
approximate the user's final mental model after a period of system usage, user
understanding continuously evolves as users interact with a system over time.
In this paper, we review many common mental model measurement techniques,
discuss tradeoffs, and recommend methods for deeper, more meaningful evaluation
of mental models when using interactive data analysis and visualization
systems. We present guidelines for evaluating mental models over time that
reveal the evolution of specific model updates and how they may map to the
particular use of interface features and data queries. By asking users to
describe what they know and how they know it, researchers can collect
structured, time-ordered insight into a user's conceptualization process while
also helping guide users to their own discoveries.Comment: 10 pages, submitted to BELIV 2020 Worksho
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Data Abstraction for Visualizing Large Time Series
Numeric time series is a class of data consisting of chronologically ordered observations represented by numeric values. Much of the data in various domains, such as financial, medical and scientific, are represented in the form of time series. To cope with the increasing sizes of datasets, numerous approaches for abstracting large temporal data are developed in the area of data mining. Many of them proved to be useful for time series visualization. However, despite the existence of numerous surveys on time series mining and visualization, there is no comprehensive classification of the existing methods based on the needs of visualization designers. We propose a classification framework that defines essential criteria for selecting an abstraction method with an eye to subsequent visualization and support of users' analysis tasks. We show that approaches developed in the data mining field are capable of creating representations that are useful for visualizing time series data. We evaluate these methods in terms of the defined criteria and provide a summary table that can be easily used for selecting suitable abstraction methods depending on data properties, desirable form of representation, behaviour features to be studied, required accuracy and level of detail, and the necessity of efficient search and querying. We also indicate directions for possible extension of the proposed classification framework
Graph-based Temporal Analysis in Digital Forensics
Establishing a timeline as part of a digital forensics investigation is a vital part of understanding the order in which system events occurred. However, most digital forensics tools present timelines as histogram or as raw artifacts. Consequently, digital forensics examiners are forced to rely on manual, labor-intensive practices to reconstruct system events. Current digital forensics analysis tools are at their technological limit with the increasing storage and complexity of data. A graph-based timeline can present digital forensics evidence in a structure that can be immediately understood and effortlessly focused. This paper presents the Temporal Analysis Integration Management Application (TAIMA) to enhance digital forensics analysis via information visualization (infovis) techniques. TAIMA is a prototype application that provides a graph-based timeline for event reconstruction using abstraction and visualization techniques. A workflow illustration and pilot usability study provided evidence that TAIMA assisted digital forensics specialists in identifying key system events during digital forensics analysis
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
A time-frequency diagram is a commonly used visualization for observing the
time-frequency distribution of radio signals and analyzing their time-varying
patterns of communication states in radio monitoring and management. While it
excels when performing short-term signal analyses, it becomes inadaptable for
long-term signal analyses because it cannot adequately depict signal
time-varying patterns in a large time span on a space-limited screen. This
research thus presents an abstract signal time-frequency (ASTF) diagram to
address this problem. In the diagram design, a visual abstraction method is
proposed to visually encode signal communication state changes in time slices.
A time segmentation algorithm is proposed to divide a large time span into time
slices.Three new quantified metrics and a loss function are defined to ensure
the preservation of important time-varying information in the time
segmentation. An algorithm performance experiment and a user study are
conducted to evaluate the effectiveness of the diagram for long-term signal
analyses.Comment: 11 pages, 9 figure
Empirically measuring soft knowledge in visualization
In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance
the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of
three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e.,
in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control
variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution
of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents
distortion more effectively than the other two classes
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