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ISSN: 1077-2626
Fan Du , Fan Du is with the Department of Computer Science and the Human-Computer Interaction Lab, University of Maryland, College Park, MD, USA 20742.
Ben Shneiderman , Fan Du is with the Department of Computer Science and the Human-Computer Interaction Lab, University of Maryland, College Park, MD, USA 20742.
Catherine Plaisant , Fan Du is with the Department of Computer Science and the Human-Computer Interaction Lab, University of Maryland, College Park, MD, USA 20742.
Sana Malik , Fan Du is with the Department of Computer Science and the Human-Computer Interaction Lab, University of Maryland, College Park, MD, USA 20742.
Adam Perer , Fan Du is with the Department of Computer Science and the Human-Computer Interaction Lab, University of Maryland, College Park, MD, USA 20742.
ABSTRACT
The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.
INDEX TERMS
temporal event sequences, Big data, temporal data,
CITATION
Fan Du, Ben Shneiderman, Catherine Plaisant, Sana Malik, Adam Perer, "Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus", IEEE Transactions on Visualization & Computer Graphics, vol. , no. , pp. 1, 5555, doi:10.1109/TVCG.2016.2539960
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