October 24, 2016. Baltimore, Maryland.
|2:00-2:10pm||Welcome from Chairs|
|2:10-2:25pm||Introductory Session with Ben Shneiderman
|2:25-3:15pm||Paper Session #1: Directions and Challenges
|3:15-4:00pm||Paper Session #2: Advanced Techniques
|4:15-4:55||Paper Session #3: Systems / Applications
|5:45-5:55||Wrap up with Co-Chairs|
|Papers||August 26, 2016 (Extended)|
|Notifications||September 8, 2016|
|Workshop||October 24, 2016|
As visualization researchers, our ongoing challenge is often to leverage the voluminous data that is being captured to drive decision making and insights. Common to such data are temporal events, data points with both a timestamp and event type, so understanding patterns of temporal event sequences is an important problem to many domains.
For example, medical researchers may wish to leverage the data captured by electronic health records to determine if certain sequences of medical events correlate with positive outcomes. Similarly, city government officials may wish to leverage the temporal data collected from their transportation systems, call centers, and law enforcement agencies to improve their cities’ services. Engineers might analyze temporal events from system logs to manage large-scale distributed system performance. Consumer services analysts might seek to understand social media and shopping patterns.
Recently, there has been an increasing amount of visualization research focusing on temporal events. Papers on this topic have appeared at IEEE VIS, other visualization conferences, and at many domain-specific venues.
The main question behind the proposed workshop is: How can we unify and advance the role of visualization in temporal event analysis? The workshop will gather visualization researchers together to discuss the interesting opportunities and challenges visualization may face with temporal events. It aims to both gather the existing literature on the subject and build a research agenda for future research.
In order to encourage collaboration and comparison, we will distribute 1-2 sample datasets before the workshop. We will encourage, but not require, presenters of papers and demos to show how their system performs on these sample datasets; this will better allow us all to discuss the tradeoffs of different designs.
This workshop aims to increase awareness about this interesting opportunity for visualization research, collect and compare examples of existing and ongoing research in this area, and to create a preliminary temporal event analytics research agenda for visualization researchers. The workshop will allow participants to showcase their existing research and ideas, and to learn and reflect on the latest advances in visualization of temporal events. Therefore, work which addresses any of the following questions would be highly relevant:
Replace the default copyright block found in the TVCG template with:
Proceedings of the IEEE VIS 2016 Workshop on Temporal & Sequential Event Analysis. Available online at: http://eventevent.github.io
During the submission process, you will be asked to select the appropriate track (Research Papers or Position Papers or Posters).
|Aguvue (Full)||Application event log data||Points|
|Aguvue (Sample)||Application event log data||Points|
|ACT Testing Data||IEEE VGTC VPG International Data-Visualization Contest of ACT Testing Centers||Points|
|Hospital Transfers||Contains the (synthetic but realistic) series of departments that patient get transferred through during their stay in the hospital.||Points|
|Professors||Contains the series of appointments, conferences, and publications that academic professionals go through as they attain full-time professor status.||Points and Intervals|
|Chicago Bulls Season||Contains the entire 2012-2013 Chicago Bulls season, including an Offense → Defense dataset, a Defense → Offense dataset, and a config file that can be loaded with either.||Points and Intervals|
|UMD vs. UNC||Contains Maryland's 2012-2013 game against UNC, including an Offense → Defense dataset, a Defense → Offense dataset, and a config file that can be loaded with either.||Points and Intervals|
Adam Perer s a research scientist at IBM T.J. Watson Research Center. His recent research focuses on visualization and analytic techniques for making sense of electronic medical records and social networks. Increasingly, he has been working on developing new techniques for visualizing frequent event sequences in temporal data. He received his Ph.D. in Computer Science from the University of Maryland.
Steven Drucker is a Principal Researcher at Microsoft Research. His recent research focuses on techniques for natural exploration and presentation of data. He has been looking at ways of democratizing visualization systems including touch interaction; facilitating designer interaction, and temporal sequence analysis.
Danyel Fisher is a Senior Researcher at Microsoft Research. His recent research focuses on big data analytics and exploratory visualization; on designer interaction, and on temporal sequence analysis. With Miriah Meyer, he is a co-author of “Making Sense of Data, to be published by O’Reilly in 2016.
David Gotz is Associate Professor of Information Science at the University of North Carolina at Chapel Hill (UNC-Chapel Hill) and Associate Director for the Carolina Health Informatics Program. His recent research focuses on visual analytics and communication methods for helping domain experts discover insights and take action based on large collections of temporal event data. He received his Ph.D. in Computer Science from UNC-Chapel Hill and was previously a Research Scientist at the IBM T.J. Watson Research Center.
Megan Monroe is a research scientist at IBM’s Cognitive User Experience lab in Cambridge, MA. She received her Ph.D. in Computer Science from the University of Maryland, focusing on the visual analysis of temporal event sequences. She has worked with temporal datasets ranging from electronic health records to process logs to sports analytics.
Catherine Plaisant is a Senior Research Scientist at the University of Maryland Institute for Advanced Computer Studies and Associate Director of Research of the Human-Computer Interaction Lab. Catherine Plaisant earned a Doctorat d’Ingenieur degree in France (similar to a Industrial Engineering PhD). Her research includes LifeLines and EventFlow for the analysis of temporal records.
Ben Shneiderman is a Distinguished University Professor at the University of Maryland Department of Computer Science and Founding Director (1983-2000) of the Human-Computer Interaction Lab. He is a member of the National Academy of Engineering and recipient of the IEEE TVCG Career Achievement Award. His work on temporal event data is largely with EventFlow.