University of Maryland

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

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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/Empowering the State through the Center for Health-related Informatics and Bioimaging.


Since 2018 Coco is available for licensing from the UMd STORE 

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You can also request a software review agreement or more information about special licensing agreements that better suit your needs (e.g. access to source code) by contacting the Office of Technology Commercialization (OTC) Email: (and we recommend cc:ing



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.
A Visual Analytics Approach to Comparing Cohorts of Event Sequences
Ph.D Dissertation from the Department of Computer Science

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