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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.

Finding the differences and similarities between two datasets is a common analytics task. With temporal event sequence data, this task is complex because of the many ways single events and event sequences can differ between the two datasets (or cohorts) of records: the structure of the event sequences (e.g., event order, co-occurring events, or event frequencies), the attributes of events and records (e.g., patient gender), or metrics about the timestamps themselves (e.g., event duration). In exploratory analyses, running statistical tests to cover all cases is time-consuming and determining which results are significant becomes cumbersome. Current analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. This paper presents a taxonomy of metrics for comparing cohorts of temporal event sequences, showing that the problem-space is bounded. We also present a visual analytics tool, CoCo (for "Cohort Comparison"), which implements balanced integration of automated statistics with an intelligent user interface to guide users to significant, distinguishing features between the cohorts. Lastly, we describe two early case studies: the first with a research team studying medical team performance in the emergency department and the second with pharmacy researchers.

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