# monroe2013temporal

# Temporal Event Sequence Simplification


The authors previously published EventFlow [1], a visualization tool that transforms an entire dataset of temporal event records into an aggregated display, allowing researchers to analyze population-level patterns and trends. As the dataset grows, it quickly becomes difficult to provide a succinct, summarizing display. EventFlow 2 [2] aims to solve this problem by providing a series of user-driven data simplifications that allow researchers to focus on the core events.

The authors worked with researchers from The US Army Pharmacovigilance Center (PVC), and epidemiologists were able to discover anomalies in their dataset immediately. Techniques are tested against 5 datasets:

  1. Long-acting β-agonists (LABA) dataset with the PVC
  2. Opioid Misclassification dataset with the PVC
  3. The Diabetic Foot with the University of Florida College of Medicine
  4. Attention Deficit Disorder Treatment with the University of Maryland School of Medicine
  5. Pediatric Trauma Procedures with MedStar Health Facilities

Two datasets were used for testing the overall performance of EventFlow 2.0, Opioid Misclassification dataset with the PVC and University of Maryland men’s basketball team's play-by-play data.


Figure 1 shows an example of using EventFlow 2.0. The left window shows the original dataset with over 2,700 visual elements, the right window shows the simplified version with 492 visual elements.

In Figure 2, user options are available on the left, the user is able to align, filter, merge and window (time and attribute filtering) the events. The aggregated and simplified events are shown in the middle, and the individual event record on the right.

Based on the work of Allen et al. [3], the authors developed an advanced graphical query system that allows users to draw out their queries using the same icons that are used to represent events in the individual record display.

Related Work

EventFlow 2.0 is built on top of EventFlow [1:1]. The authors review the approaches proposed in the survey of interactive visualization systems of EHR by Rind et al. [4]. Aigner et al. introduce various techniques for visualizing different kinds of temporal data in their book [5] which informs the temporal visualization of EventFlow.

Temporal logic, refers to the logical representation of interval events is inspired by Allen et al. [3:1].

Temporal querying techniques for exploring large dataset are adopted from the work of Jensen et al. [6] and Snodgrass [7].

Data Characteristics

  • Data source: LABA dataset from The US Army Pharmacovigilance Center (PVC)
  • Size: unknown, 250 visualized
  • Spatial dimensionality: 2D
  • Temporal dimensionality: static
  • Type: multivariate

Papers Cited


Years Spanned


Application Domain

  • Electronic Health Record Visualization
  • Medical focus: Pharmacology


  1. Monroe, M., Wongsuphasawat, K., Plaisant, C., Shneiderman, B., Millstein, J., & Gold, S. (2012). Exploring Point and Interval Event Patterns: Display Methods and Interactive Visual Query. HCIL Technical Report, Dept Computer Science, University of Maryland. ↩︎ ↩︎

  2. Monroe, M., Lan, R., Lee, H., Plaisant, C., & Shneiderman, B. (2013). Temporal event sequence simplification. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2227–2236. https://doi.org/10.1109/TVCG.2013.200 ↩︎

  3. Allen, J. F., & Ferguson, G. (1994). Actions and events in interval temporal logic. Journal of Logic and Computation. https://doi.org/10.1093/logcom/4.5.531 ↩︎ ↩︎

  4. Rind, A., Wang, T. D., Aigner, W., Miksch, S., Wongsuphasawat, K., Plaisant, C., & Shneiderman, B. (2011). Interactive Information Visualization to Explore and Query Electronic Health Records. Foundations and Trends® in Human–Computer Interaction, 5(3), 207–298. https://doi.org/10.1561/1100000039 ↩︎

  5. Aigner, W., Miksch, S., Schumann, H., & Tominski, C. (2011). Visualization of Time-Oriented Data. In Human-Computer Interaction. https://doi.org/10.1007/978-0-85729-079-3 ↩︎

  6. Jensen, C. S., Shashi, C., Fabio, K. G., Kalua, P. P., Kline, N., Montanari, A., … Wuu, G. T. J. (1993). The TSQL Benchmark. Proceedings of the International Workshop on an Infrastructure for Temporal Databases, (January). ↩︎

  7. Snodgrass, R. (1984). The temporal query language tQuel. Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. https://doi.org/10.1145/588011.588041 ↩︎

🔄 Last Updated: 7/8/2020, 9:48:18 PM