Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization

Event sequence data is common in many domains, ranging from electronic medical records (EMRs) to sports events. Moreover, such sequences often result in measurable outcomes (e.g., life or death, win or loss). Collections of event sequences can be aggregated together to form event progression pathways. These pathways can then be connected with outcomes to model how alternative chains of events may lead to different results. This paper describes the Outflow visualization technique, designed to (1) aggregate multiple event sequences, (2) display the aggregate pathways through different event states with timing and cardinality, (3) summarize the pathways' corresponding outcomes, and (4) allow users to explore external factors that correlate with specific pathway state transitions. Results from a user study with twelve participants show that users were able to learn how to use Outflow easily with limited training and perform a range of tasks both accurately and rapidly.

[1]  Helwig Hauser,et al.  Parallel Sets: interactive exploration and visual analysis of categorical data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[2]  Matthew D. Cooper,et al.  Everyday Life Discoveries: Mining and Visualizing Activity Patterns in Social Science Diary Data , 2007, 2007 11th International Conference Information Visualization (IV '07).

[3]  Mitsuhiko Toda,et al.  Methods for Visual Understanding of Hierarchical System Structures , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Ben Shneiderman,et al.  Querying event sequences by exact match or similarity search: Design and empirical evaluation , 2012, Interact. Comput..

[5]  Andreas Paepcke,et al.  Progressive multiples for communication-minded visualization , 2007, GI '07.

[6]  Ben Shneiderman,et al.  Finding comparable temporal categorical records: A similarity measure with an interactive visualization , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[7]  Jarke J. van Wijk,et al.  BaobabView: Interactive construction and analysis of decision trees , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[8]  Otto-von-Guericke.  Connecting Time-Oriented Data and Information to a Coherent Interactive Visualization , 2004 .

[9]  P. Riehmann,et al.  Interactive Sankey diagrams , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[10]  Jarke J. van Wijk,et al.  Eurographics/ Ieee-vgtc Symposium on Visualization 2008 Visual Inspection of Multivariate Graphs , 2022 .

[11]  Michael G. Kahn,et al.  The visual display of temporal information , 1991, Artif. Intell. Medicine.

[12]  Ben Shneiderman,et al.  A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[13]  Taylor L. Booth,et al.  Sequential machines and automata theory , 1967 .

[14]  Matthew D. Cooper,et al.  ActiviTree: Interactive Visual Exploration of Sequences in Event-Based Data Using Graph Similarity , 2009, IEEE Transactions on Visualization and Computer Graphics.

[15]  Ioannis G. Tollis,et al.  Algorithms for Drawing Graphs: an Annotated Bibliography , 1988, Comput. Geom..

[16]  Michael Friendly,et al.  Visions and Re-Visions of Charles Joseph Minard , 2002 .

[17]  Ben Shneiderman,et al.  Aligning temporal data by sentinel events: discovering patterns in electronic health records , 2008, CHI.

[18]  Jarke J. van Wijk,et al.  Visual Analysis of Multivariate State Transition Graphs , 2006, IEEE Transactions on Visualization and Computer Graphics.

[19]  Ben Shneiderman,et al.  LifeFlow: visualizing an overview of event sequences , 2011, CHI.

[20]  P. Hanrahan,et al.  Flow map layout , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[21]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[22]  Michael Burch,et al.  Timeline trees: visualizing sequences of transactions in information hierarchies , 2008, AVI '08.

[23]  C. A. Petri.  Communication with automata , 1966 .

[24]  Gerald M. Karam,et al.  Visualization using timelines , 1994, ISSTA '94.

[25]  Marc A Pfeffer,et al.  Heart failure , 2005, The Lancet.

[26]  Martin Wattenberg,et al.  Visual exploration of multivariate graphs , 2006, CHI.

[27]  Monica M. C. Schraefel,et al.  Continuum: designing timelines for hierarchies, relationships and scale , 2007, UIST.

[28]  Jarke J. van Wijk,et al.  Interactive Visualization of State Transition Systems , 2002, IEEE Trans. Vis. Comput. Graph..

[29]  Stefan Biffl,et al.  PlanningLines: novel glyphs for representing temporal uncertainties and their evaluation , 2005, Ninth International Conference on Information Visualisation (IV'05).

[30]  Ben Shneiderman,et al.  LifeLines: using visualization to enhance navigation and analysis of patient records , 1998, AMIA.

[31]  W. Kannel,et al.  The natural history of congestive heart failure: the Framingham study. , 1971, The New England journal of medicine.

[32]  Robert S. Laramee,et al.  Smooth Graphs for Visual Exploration of Higher-Order State Transitions , 2009, IEEE Transactions on Visualization and Computer Graphics.

[33]  Beverly L. Harrison,et al.  Timelines: An Interactive System for the Collection and Visualization of Temporal Data , 1994 .

[34]  Krist Wongsuphasawat,et al.  Outflow : Visualizing Patient Flow by Symptoms and Outcome , 2011 .