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Journal Article

Comparative Evaluation of an Interactive Time-Series Visualization that Combines Quantitative Data w

TL;DR: A controlled experiment is reported on a controlled experiment that compares this technique with another visualization method used in the well‐known KNAVE‐II framework, both of which integrate quantitative data with qualitative abstractions.
Abstract: In many application areas, analysts have to make sense of large volumes of multivariate time‐series data. Explorative analysis of this kind of data is often difficult and overwhelming at the level of raw data. Temporal data abstraction reduces data complexity by deriving qualitative statements that reflect domain‐specific key characteristics. Visual representations of abstractions and raw data together with appropriate interaction methods can support analysts in making their data easier to understand. Such a visualization technique that applies smooth semantic zooming has been developed in the context of patient data analysis. However, no empirical evidence on its effectiveness and efficiency is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares this technique with another visualization method used in the well‐known KNAVE‐II framework. Both methods integrate quantitative data with qualitative abstractions whereas the first one uses a composite representation with color‐coding to display the qualitative data and spatial position coding for the quantitative data. The second technique uses juxtaposed representations for quantitative and qualitative data with spatial position coding for both. Results show that the test persons using the composite representation were generally faster, particularly for more complex tasks that involve quantitative values as well as qualitative abstractions.
Citations
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Book
27 Feb 2013
TL;DR: This monograph is written for both scientific researchers and designers of future user interfaces for EHRs to help them understand this vital domain and appreciate the features and virtues of existing systems, so they can create still more advanced systems.
Abstract: Physicians are confronted with increasingly complex patient histories based on which they must make life-critical treatment decisions. At the same time, clinical researchers are eager to study the growing databases of patient histories to detect unknown patterns, ensure quality control, and discover surprising outcomes. Designers of Electronic Health Record systems (EHRs) have great potential to apply innovative visual methods to support clinical decision-making and research. This work surveys the state-of-the-art of information visualization systems for exploring and querying EHRs, as described in the scientific literature. We examine how systems differ in their features and highlight how these differences are related to their design and the medical scenarios they tackle. The systems are compared on a set of criteria: (1) data types covered, (2) multivariate analysis support, (3) number of patient records used (one or multiple), and (4) user intents addressed. Based on our survey and evidence gained from evaluation studies, we believe that effective information visualization can facilitate analysis of EHRs for patient treatment and clinical research. Thus, we encourage the information visualization community to study the application of their systems in health care. Our monograph is written for both scientific researchers and designers of future user interfaces for EHRs. We hope it will help them understand this vital domain and appreciate the features and virtues of existing systems, so they can create still more advanced systems. We identify potential future research topics in interactive support for data abstraction, in systems for intermittent users, such as patients, and in more detailed evaluations.

212 citations


Cites background from "Comparative Evaluation of an Intera..."

  • ...226 4.1 Visualization of a Single Patient Record 227...

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Proceedings ArticleDOI
26 Apr 2014
TL;DR: This paper combines prior results from perceptual science and graphical perception to suggest a set of design variables that influence performance on various aggregate comparison tasks, and describes how choices in these variables can lead to designs that are matched to particular tasks.
Abstract: Many visualization tasks require the viewer to make judgments about aggregate properties of data. Recent work has shown that viewers can perform such tasks effectively, for example to efficiently compare the maximums or means over ranges of data. However, this work also shows that such effectiveness depends on the designs of the displays. In this paper, we explore this relationship between aggregation task and visualization design to provide guidance on matching tasks with designs. We combine prior results from perceptual science and graphical perception to suggest a set of design variables that influence performance on various aggregate comparison tasks. We describe how choices in these variables can lead to designs that are matched to particular tasks. We use these variables to assess a set of eight different designs, predicting how they will support a set of six aggregate time series comparison tasks. A crowd-sourced evaluation confirms these predictions. These results not only provide evidence for how the specific visualizations support various tasks, but also suggest using the identified design variables as a tool for designing visualizations well suited for various types of tasks.

89 citations


Cites background from "Comparative Evaluation of an Intera..."

  • ...More recently experiments have considered higher-level tasks and the perception of aggregate properties [13, 1, 19, 21]....

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  • ...[1] found that composite visualization techniques that leverage both color and position encodings better support multiple simultaneous judgment tasks than traditional techniques....

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Journal ArticleDOI
TL;DR: A number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow are developed, so that the resultant image becomes appropriate for data storytelling.
Abstract: Visualization is a powerful technique for analysis and communication of complex, multidimensional, and time-varying data. However, it can be difficult to manually synthesize a coherent narrative in a chart or graph due to the quantity of visualized attributes, a variety of salient features, and the awareness required to interpret points of interest (POls). We present Temporal Summary Images (TSIs) as an approach for both exploring this data and creating stories from it. As a visualization, a TSI is composed of three common components: (1) a temporal layout, (2) comic strip-style data snapshots, and (3) textual annotations. To augment user analysis and exploration, we have developed a number of interactive techniques that recommend relevant data features and design choices, including an automatic annotations workflow. As the analysis and visual design processes converge, the resultant image becomes appropriate for data storytelling. For validation, we use a prototype implementation for TSIs to conduct two case studies with large-scale, scientific simulation datasets.

79 citations


Cites background or methods from "Comparative Evaluation of an Intera..."

  • ...The set of saved annotations acts as a way to help narrate trends or highlights to TSI viewers....

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  • ...We focus the TSI process on the first two points: exploring the data and creating a presentation-quality visualization....

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Journal ArticleDOI
TL;DR: A three-dimensional conceptual space of user tasks in visualization, referred to as the task cube, is identified and the more precise concepts ‘objective’ and ‘action’ for tasks are identified.
Abstract: User tasks play a pivotal role in visualization design and evaluation. However, the term ‘task’ is used ambiguously within the visualization community. In this article, we critically analyze the re...

58 citations


Cites methods from "Comparative Evaluation of an Intera..."

  • ...The SemanticTimeZoom technique [3] was evaluated using 12 problems :::::::: objectives, which were categorized based on the Andrienko and Andrienko [7] problem ::::::: objective : framework....

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Journal ArticleDOI
TL;DR: Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.
Abstract: Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans in which the body stops producing insulin and blood glucose levels rise. The goal of intensive diabetes management is to lower average blood glucose through frequent adjustments to insulin protocol, diet, and behavior. Manual logs and medical device data are collected by patients, but these multiple sources are presented in disparate visualization designs to the clinician—making temporal inference difficult. We conducted a design study over 18 months with clinicians performing intensive diabetes management. We present a data abstraction and novel hierarchical task abstraction for this domain. We also contribute IDMVis: a visualization tool for temporal event sequences with multidimensional, interrelated data. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. We validate our design decisions based on our domain abstractions, best practices, and through a qualitative evaluation with six clinicians. The results of this study indicate that IDMVis accurately reflects the workflow of clinicians. Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.

58 citations


Cites background from "Comparative Evaluation of an Intera..."

  • ...Overview and detail approaches are beneficial in these cases [2, 45]....

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References
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Proceedings ArticleDOI
03 Sep 1996
TL;DR: A task by data type taxonomy with seven data types and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts) is offered.
Abstract: A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts).

5,290 citations


"Comparative Evaluation of an Intera..." refers background in this paper

  • ...Visualization of time-series data is a prominent research area [Shn96, AMST11]....

    [...]

Book
04 Feb 2000
TL;DR: The art and science of why the authors see objects the way they do are explored, and the author presents the key principles at work for a wide range of applications--resulting in visualization of improved clarity, utility, and persuasiveness.
Abstract: Most designers know that yellow text presented against a blue background reads clearly and easily, but how many can explain why, and what really are the best ways to help others and ourselves clearly see key patterns in a bunch of data? When we use software, access a website, or view business or scientific graphics, our understanding is greatly enhanced or impeded by the way the information is presented. This book explores the art and science of why we see objects the way we do. Based on the science of perception and vision, the author presents the key principles at work for a wide range of applications--resulting in visualization of improved clarity, utility, and persuasiveness. The book offers practical guidelines that can be applied by anyone: interaction designers, graphic designers of all kinds (including web designers), data miners, and financial analysts. Complete update of the recognized source in industry, research, and academic for applicable guidance on information visualizing. Includes the latest research and state of the art information on multimedia presentation. More than 160 explicit design guidelines based on vision science. A new final chapter that explains the process of visual thinking and how visualizations help us to think about problems. Packed with over 400 informative full color illustrations, which are key to understanding of the subject. Table of Contents Chapter 1. Foundations for an Applied Science of Data Visualization Chapter 2. The Environment, Optics, Resolution, and the Display Chapter 3. Lightness, Brightness, Contrast and Constancy Chapter 4. Color Chapter 5. Visual Salience and Finding Information Chapter 6. Static and Moving Patterns Chapter 7. Space Perception Chapter 8. Visual Objects and Data Objects Chapter 9. Images, Narrative, and Gestures for Explanation Chapter 10. Interacting with Visualizations Chapter 11. Visual Thinking Processes

3,837 citations

01 Jan 2005
TL;DR: This is the book form of the Research and Development Agenda for Visual Analytics to be published by IEEE in 2005.
Abstract: This is the book form of the Research and Development Agenda for Visual Analytics to be published by IEEE in 2005.

1,978 citations

Journal ArticleDOI
TL;DR: The utility of the new symbolic representation of time series formed is demonstrated, which allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measuresdefined on the original series.
Abstract: Many high level representations of time series have been proposed for data mining, including Fourier transforms, wavelets, eigenwaves, piecewise polynomial models, etc. Many researchers have also considered symbolic representations of time series, noting that such representations would potentiality allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities. While many symbolic representations of time series have been introduced over the past decades, they all suffer from two fatal flaws. First, the dimensionality of the symbolic representation is the same as the original data, and virtually all data mining algorithms scale poorly with dimensionality. Second, although distance measures can be defined on the symbolic approaches, these distance measures have little correlation with distance measures defined on the original time series. In this work we formulate a new symbolic representation of time series. Our representation is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measures defined on the original series. As we shall demonstrate, this latter feature is particularly exciting because it allows one to run certain data mining algorithms on the efficiently manipulated symbolic representation, while producing identical results to the algorithms that operate on the original data. In particular, we will demonstrate the utility of our representation on various data mining tasks of clustering, classification, query by content, anomaly detection, motif discovery, and visualization.

1,452 citations

Journal ArticleDOI
TL;DR: colorBrewer is an online tool designed to take some of the guesswork out of this process by helping users select appropriate colour schemes for their specific mapping needs by considering the number of data classes, nature of their data, and the end-use environment for the map.
Abstract: Choosing effective colour schemes for thematic maps is surprisingly difficult. ColorBrewer is an online tool designed to take some of the guesswork out of this process by helping users select appro...

1,089 citations