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Title: Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis

Abstract

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conducting a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Pacific Northwest National Laboratory, Richland Washington USA
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1457598
Report Number(s):
PNNL-SA-115628
Journal ID: ISSN 0167-7055
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Computer Graphics Forum
Additional Journal Information:
Journal Volume: 37; Journal Issue: 1; Journal ID: ISSN 0167-7055
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
streaming data; visual analytics; survey

Citation Formats

Dasgupta, Aritra, Arendt, Dustin L., Franklin, Lyndsey R., Wong, Pak Chung, and Cook, Kristin A. Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis. United States: N. p., 2017. Web. doi:10.1111/cgf.13264.
Dasgupta, Aritra, Arendt, Dustin L., Franklin, Lyndsey R., Wong, Pak Chung, & Cook, Kristin A. Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis. United States. doi:10.1111/cgf.13264.
Dasgupta, Aritra, Arendt, Dustin L., Franklin, Lyndsey R., Wong, Pak Chung, and Cook, Kristin A. Fri . "Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis". United States. doi:10.1111/cgf.13264.
@article{osti_1457598,
title = {Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis},
author = {Dasgupta, Aritra and Arendt, Dustin L. and Franklin, Lyndsey R. and Wong, Pak Chung and Cook, Kristin A.},
abstractNote = {State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conducting a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.},
doi = {10.1111/cgf.13264},
journal = {Computer Graphics Forum},
issn = {0167-7055},
number = 1,
volume = 37,
place = {United States},
year = {2017},
month = {9}
}