University of Maryland

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

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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

Since 2018 Coco is available for licensing from the UMd STORE 

Not sure? Special cases?
Contact plaisant@cs.umd.edu and describe your affiliation and project.

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: otc@umd.edu (and we recommend cc:ing plaisant@cs.umd.edu)

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