Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus
Issue No. 06 - June (2017 vol. 23)
Fan Du , Department of Computer Science and the Human-Computer Interaction LabUniversity of Maryland
Ben Shneiderman , Department of Computer Science and the Human-Computer Interaction LabUniversity of Maryland
Catherine Plaisant , UMIACS and the Human-Computer Interaction LabUniversity of Maryland
Sana Malik , Department of Computer Science and the Human-Computer Interaction LabUniversity of Maryland
Adam Perer , IBM T.J. Watson Research Center
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.
Focusing, Visual analytics, Data visualization, Medical services, Electronic mail, Cleaning, Sequences
F. Du, B. Shneiderman, C. Plaisant, S. Malik and A. Perer, "Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus," in IEEE Transactions on Visualization & Computer Graphics, vol. 23, no. 6, pp. 1636-1649, 2017.