CoCo: A Visual Analytics Tool for Comparing Cohorts of Event Sequences



CoCo is a visual analytics tool that enables users to compare two sets of temporal sequence data. It combines automated statistical tests with user-guidance to enable insights, hypothesis generation, and much more. Users see (1) statistics about their dataset, (2) event-level statistics, and (3) a menu of metrics. CoCo displays significance tests in a unified form for measures such as prevalence and duration of gaps.



  • Ebere Onukwugha, University of Maryland School of Pharmacy
  • Rachel Webman, Children’s National Medical Center
  • Leah Macfadyen, University of British Columbia
  • Margret Bjarnadottir, University of Maryland, Robert H. Smith School of Business
  • Eunyee Koh, Adobe Research



We gratefully acknowledge the partial funding provided by Oracle Corporation, Adobe Systems, and the University of Maryland/Mpowering the State through the Center for Health-related Informatics and Bioimaging.


We can provide you with a review version of CoCo:

  • For non-commercial use: please contact with a description of your project and organization.
  • For commercial use: EventFlow is available for licensing. To request a review copy of CoCo and for more information about licensing please contact:
    Office of Technology Commercialization (OTC)
    2130 Mitchell Building, University of Maryland,
    College Park, MD 20742
    Phone: 301-405-3947 | Fax: 301-314-9502
  • Not sure? Contact


    Malik, S., Shneiderman, B., Du, F., Plaisant, C., Bjarnadottir, M.
    High-Volume Hypothesis Testing: Systematic Exploration of Event Sequence Comparisons
    ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 6 Issue 1, May 2016, Article No. 9
    ACM New York, NY, USA

    Malik, S., Koh, E.
    High-Volume Hypothesis Testing for Large-Scale Web Log Analysis
    in Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA ’16).
    San Jose, CA, USA (2016). p. 1583-1590

    Malik, S., Du, F., Monroe, M., Onukwugha, E., Plaisant, C., Shneiderman, B.
    Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics
    in Proceedings of ACM Intelligent User Interfaces (IUI) 2015. Atlanta, GA, USA. (2015) p. 38-49

    Malik, S., Du, F., Monroe, M., Onukwugha, E., Plaisant, C., Shneiderman, B.
    An Evaluation of Visual Analytics Approaches to Comparing Cohorts of Event Sequences
    EHRVis Workshop on Visualizing Electronic Health Record Data,, Paris (2014) p. 1-6