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

• Published in: Computer graphics forum Vol. 37; no. 1
• Main Authors: Dasgupta, Aritra, Arendt, Dustin L., Franklin, Lyndsey R., Wong, Pak Chung, Cook, Kristin A.
• Format: Journal Article
• Language: English
• Published: United States Wiley 01.09.2017
• Subjects: streaming data; survey; visual analytics
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.13264

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.