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

Abstract

Real-world systems change continuously and across domains like traffic monitoring, cyber security, etc., such changes occur within short time scales. This leads to a streaming data problem and produces unique challenges for the human in the loop, as analysts have to ingest and make sense of dynamic patterns in real time. In this paper, our goal is to study how the state-of-the-art in streaming data visualization handles these challenges and reflect on the gaps and opportunities. To this end, we have three contributions: i) problem characterization for identifying domain-specific goals and challenges for handling streaming data, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space, and iii) reflections on the perceptually motivated design challenges and potential research directions for addressing them.

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:
1430411
Report Number(s):
PNNL-SA-116789
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

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_1430411,
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 = {Real-world systems change continuously and across domains like traffic monitoring, cyber security, etc., such changes occur within short time scales. This leads to a streaming data problem and produces unique challenges for the human in the loop, as analysts have to ingest and make sense of dynamic patterns in real time. In this paper, our goal is to study how the state-of-the-art in streaming data visualization handles these challenges and reflect on the gaps and opportunities. To this end, we have three contributions: i) problem characterization for identifying domain-specific goals and challenges for handling streaming data, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space, and iii) reflections on the perceptually motivated design challenges and potential research directions for addressing them.},
doi = {10.1111/cgf.13264},
journal = {Computer Graphics Forum},
issn = {0167-7055},
number = 1,
volume = 37,
place = {United States},
year = {2017},
month = {9}
}