EventFlow: Visual Analysis of Temporal Event Sequences

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EventFlow is a novel tool for event analytics.
It provides a way to:

  •  Visualize and review the data from individual records and their event sequences
  •   Search for temporal patterns of interest, using a powerful graphical interface
  •   Summarize all the event sequences, their timing and prevalence, and find anomalies
  •   Perform data transformations to reveal useful patterns that answer questions you have
  •   Select cohorts of interest for further studies

Other keywords: temporal analysis, time series, visual data mining, temporal visualization,

EventFlow Applications

Eventflow has been used for medical research, log analysis, cybersecurity, sports analytics, learning analytics, incident management, workflow analysis, pharmacovigilance, epidemiology etc. The analysis of healthcare data has been our main focus in term of number of partners.

Medical researchers and hospitals have applied EventFlow to analyze treatment patterns and outcomes in Electronic Health Records or claims reports, while network security analysts have studied cyberattack patters and sports analysts have found novel approaches to studying games, overall team performance, and seasonal patterns. Education researchers and universities have used EventFlow to look at class enrollment sequences and student records. Applications for web log, sensor data, business processes, and financial transactions are also emerging markets.
If you are interested in how EventFlow is being used look at some of the case study reports in the Publications section, or take a look at the workshops from 20162015; and 2014 which we ran in association with the Annual HCIL Symposium,

Project History

The HCIL’s ongoing work with temporal event records has produced powerful tools for analyzing and exploring patterns of point-based events: David Wang’s Lifelines2 introduced simple operators to manipulate and visualize colections of records. Krist Wongsuphasawat’s LifeFlow introduced a method for summarizing all patterns. Megan Monroe pushed the limits further with EventFlow, which now deals with interval events, for example, the 3-month interval during which patients took a medication.  Interval-based events represent a fundamental increase in complexity at every level of the application. Megan also added a powerful graphical search and (along with other HCIL students) expanded the panoply of data manipulations tools available, leading to the development of strategies for dealing with data volume and diversity.


Major past participants 

Other participants and collaborators

  • David Wang, PhD Candidate, Computer Science
  • John Alexis Gerra Gómez, PhD Candidate, Computer Science
  • Matt Mauriello, PhD Candidate, Computer Science
  • Hsueh-Chien Cheng, PhD Candidate, Computer Science
  • Hanseung Lee, PhD Candidate, Computer Science
  • Christopher Imbriano, MS Candidate, Computer Science
  • Rongjian Lan, PhD Candidate, Computer Science
  • Sigfried Gold
  • Jeff Millstein

We also appreciate the collaboration of clinical researchers and epidemiologists at the US Army Pharmacovigilance Center, University of Maryland School of Pharmacy, National Children Hospital, University of Florida, Washington Hospital Center, and many others.


We appreciate the partial support of Oracle Corporation and Adobe Corporation. Past funding was provided by the Center for Health-related Informatics and Bioimaging (CHIB) at the University of Maryland. EventFlow also builds on early work which was funded in part by NIH – National Cancer Institute grant RC1-CA147489 “Interactive Exploration of Temporal Patterns in Electronic Health Records” (for LifeLines2 and LifeFlow), and later by the Maryland Industrial Partnerships (MIPS) program and Pulse8.


This video provides a good overview of how the EventFlow aggregation is constructed, and how the search and replace can be used to manipulate the data to answer questions ↓ Or take a look at the analysis of BASKETBALL
data: Basketball Play-by-play Analysis ↓
The Offensive Rebounding of the Indiana Pacers ↓  Or see an older demo that starts with a different example (exploring patient paths after they enter the Emergency Room – e.g. looking for bounce back) ↓

Want to use EventFlow?

  • For non-commercial use: please contact eventflow.umd@gmail.com with a description of your project and organization and we will provide you with download information.
  • For commercial use: EventFlow is available for licensing. To request a review copy of EventFlow 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

User Support


Full Papers

[BEST REFERENCE] Simplification of temporal event sequences:
Megan Monroe, Rongjian Lan, Catherine Plaisant, Ben Shneiderman
Temporal Event Sequence Simplification
TVCG: IEEE Transactions on Visualization and Computer Graphic, 2013.

The EventFlow graphical query interface:
Megan Monroe, Rongjian Lan, Juan Morales del Olmo, Catherine Plaisant, Ben Shneiderman, Jeff Millstein
The Challenges of Specifying Intervals and Absences in Temporal Queries: A Graphical Language Approach.
CHI: In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2013.

Strategies used by analysts:
Fan Du, Ben Shneiderman, Catherine Plaisant, Sana Malik, Adam Perer
Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus

IEEE Transactions on Visualization and Computer Graphic, 2016 .

Technical paper on temporal query processing algorithms
Megan Monroe, Amol Deshpande
An Integer Programming Approach to Temporal Pattern Matching Queries
SSTDM: In Proceedings of International Workshop on Spatial and Spatiotemporal Data Mining, 2013.

Examples of Use

Patterns of activities of older adults
Chung, J., Ozkaynak, M. and Demiris, G.
Examining daily activity routines of older adults using workflow
Journal of Biomedical Informatics71 (2017) pp.82-90.

Innovation trajectory analysis
C. Scott Dempwolf, Ben Shneiderman
Event Analytics for Innovation Trajectories: Understanding Inputs and Outcomes for Entrepreneurial Success
Technology and Innovation (2017) 397-413.  http://dx.doi.org/10.21300/19.1.2017.397

Disease and treatment analysis (joint work with CDC)
Karlyn Beer, Sarah Collier, Fan Du, Julia Gargano
Giardiasis Diagnosis and Treatment Practices Among Commercially Insured Persons in the United States
Clinical Infectious Diseases, 64, 9 (2017) 1244-1250.

Hypothesis generation
Eberechukwu Onukwugha, Margret Bjarnadottir, Shujia Zhou, David Czerwinski
Visualizing Data for Hypothesis Generation Using Large-Volume Claims Data
Value & Outcomes Spotlight, 3, 1, 2017.

Motion capture data analysis
Jürgen Bernard, Eduard Dobermann, Anna Vögele, Björn Krüger, Jörn Kohlhammer, Dieter Fellner
Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data
Electronic Imaging, Visualization and Data Analysis, pp 34-45(12), 2017.

Prescriptions adherence analysis
Bjarnadottir, M., Malik, S., Onukwugha, E., Gooden, T., Plaisant, C.
Understanding Adherence and Prescription Patterns Using Large Scale Claims Data
PharmacoEconomics, Volume 34, Issue 2, pp 169-179, 2016.

Health service utilization among cancer patients (part of Book Chapter)
Onukwugha, E., Plaisant, C., Shneiderman, B.
Data Visualization Tools for Investigating Health Services Utilization Among Cancer Patients
Book chapter in Hesse, B., Ahern, D., and Beckjord, E. (Eds.) Oncology Informatics, Elsevier, 2016 (to appear).

Workflow for pediatric asthma patients
Mustafa Ozkaynak, Oliwier Dziadkowiec, Rakesh Mistry, Tiffany Callahan, Ze He, Sara Deakyne, Eric Tham
Characterizing Workflow for Pediatric Asthma Patients in Emergency Departments Using Electronic Health Records
Journal of Biomedical Informatics, Volume 57, pp 386-398, 2015.

US Army asthma case study (detailed version, part of a Book Chapter)
Catherine Plaisant, Megan Monroe, Tamra Meyer, Ben Shneiderman
Interactive Visualization
Book Chapter in Big Data and Health Analytics, Marconi, K. and  Lehman, H. (Eds), CRC Press – Taylor and Francis, pp 243-262, 2014.

Case study of our collaboration with the US Army Pharmacovigilance Center
Megan Monroe, Tamra Meyer, Catherine Plaisant, Rongjian Lan, Krist Wongsuphasawat, Trinka Coster, Sigfried Gold, Jeff Millstein, Ben Shneiderman.
A Pilot Study of Asthma Medications in the Military Health System.
VAHC: In Proceedings of Workshop on Visual Analytics in HealthCare, 2013.

Trauma resuscitation workflow
Carter, E., Burd, R., Monroe, M., Plaisant, C., Shneiderman, B.
Using EventFlow to Analyze Task Performance During Trauma Resuscitation
In Proceedings of the Workshop on Interactive Systems in Healthcare (WISH), 2013.

Short Papers

Presented at the EventEvent workshop at VIS 2016
Ben Shneiderman
The Event Quartet: How Visual Analytics Works for Temporal Data
Catherine Plaisant, Ben Shneiderman
The Diversity of Data and Tasks in Event Analytics (companion website with slides)

Late-breaking work on temporal event sequence overview simplification
Matthew Louis Mauriello, Ben Shneiderman, Fan Du, Sana Malik, Catherine Plaisant
Simplifying Overviews of Temporal Event Sequences (Best Paper Honorable Mention)

Early work on temporal event sequence search and replace
Rongjian Lan, Hanseung Lee, Megan Monroe, Allan Fong, Catherine Plaisant, Ben Shneiderman
Temporal Search and Replace: An Interactive Tool for the Analysis of Temporal Event Sequences

Early report on extending the Lifeflow overview visualization to handle interval data
Megan Monroe, Krist Wongsuphasawat, Catherine Plaisant, Ben Shneiderman, Jeff Millstein and Sigfried Gold
Exploring Point and Interval Event Patterns: Display Methods and Interactive Visual Query

Other publications written by our case study partners

E. Onukwugha, Y. Kwok, C. Yong, C. Mullins, B. Seal, A. Hussain
Variation in the length of radiation therapy among men diagnosed with Incident Metastatic Prostate Cancer”
ASTRO: Poster presented at the 2013 American Society for Radiation Oncology meeting.

* See also related HCIL Workshops (20162015; and 2014) , as they included presentations from EventFlow users.

General survey paper

Rind, A., Wang, T., Aigner, W., Miksch, S., Wongsuphasawat, K., Plaisant, C., Shneiderman, B.
Interactive Information Visualization for Exploring and Querying Electronic Health Records: A Systematic Review
Foundations and Trends in Human-Computer Interaction, Vol. 5, No. 3, 207-298, 2013.


Hunter Whitney, It’s About Time, UX Magazine, September 2014.

Products and papers stimulated by this work

Our original research on LifeFlow and EventFlow stimulated new work by other labs, such as:

Related projects and events

Presentations (list non updated)

The following slides provide an introduction to the motivation behind EventFlow, and a summary of its features.

A full description can be found in the tech report.

Other Presentations