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

coco-overview-v6

PROJECT DESCRIPTION

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.

PARTICIPANTS

Partners

  • 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

NEWS

SPONSORS

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.

SOFTWARE

We can provide you with a review version of CoCo:

    • For non-commercial use: please contact eventflow.umd@gmail.com 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
      Email: umdtechtransfer@umd.edu
      URL: www.otc.umd.edu
    • Not sure? Contact eventflow.umd@gmail.com

VIDEOS

PAPERS

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
[Paper]

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
[Paper]

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
[Paper]

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, www.cs.umd.edu/hcil/parisehrvis, Paris (2014) p. 1-6
[Paper]