Allan Fong Hanseung Lee Rongjian Lan University of M aryland Department of Computer Science

1. Temporal Search and Replace: A novel tool to simplify event sequences in large complex temporal datasets Allan Fong Hanseung Lee RongjianLan University of Maryland Department of Computer Science

2. Outline • Background • Our Contributions • Temporal Search and Replace Features • User Interface & Interaction Description • Demo • Search Algorithm • Conclusions and Future Work

3. Background • Motivation: • Simplify the visualization of large complex temporal datasets • Related works: • Search and replace research for graphics [Kurlander 1992] [Yeh 2006] • Psychology research • Miller’s 7 +/- 2 and chunking [Miller 1956] • Temporal chunking (clustering) [Farrell 2012] • Gestalt perception (202) 456 - 2121

4. Our Contributions • Goal: • Develop and integrate a Temporal Search and Replace tool into EventFlow • Contributions: • Replace capabilities in EventFlow • Search capability for repeating sequences • Search capability for no ordered sequences • Search capability using wildcard notations • Customers/End Users: • Megan Monroe and Catherine Plaisant(UMD HCIL) • Sheila Weiss (UMD Baltimore)

5. Temporal Search and Replace Features • One time stamp (e.g., one day) • Journal Published: 5/1/10 • Two time stamps (e.g., start time and an end time) • Professor: 9/5/09 - 9/30/11 ≥2 { , = , …} ≥4 { , , = , , , }

6. Visualizing Nested Constraints • Reverse (BFS) Breadth-First Search is used to plot inner constraint boxes & ovals first • Dynamic add / remove constraints are easily done using the following property • All nodes on the same level are disjoint sets • A parent node contains the range of all its child nodes Constraints: Indices: Events:

7. User Interface & Interaction

8. Demo

9. Search Algorithm • Repetition • We extend the original EventFlow fixed-length pattern search algorithm(Temporal Pattern Search - TPS) to support flexible pattern searching. • Search index is repeatedly reset so a pattern can be matched multiple times • No Order • Instead of permuting the events and do sequential event matching, keep matching greedily until each event in the constraint is matched for once. • Wildcard • Finding any event after the current time in the record

10. Embedded Constraints • Using tree structure to store the embedded constraints • Using stack to store the active constraints Constraints: Indices: Events: Stack

11. Conclusions and Future Work • Introduce a novel temporal event search and replace tool • Extend EventFlow’s search algorithm to support repetition, no order and wildcard constraints. • Conduct user study with 9 participants • Future work includes: • Add rules that better suit the purpose of simplification of temporal events. • Polish the user interface to reduce the stress of query formulation. • Pattern-level find and replace.

12. Acknowledgements • Megan Monroe, Catherine Plaisant, and Ben Shneiderman • Sheila Weiss • Kent Norman • And for all the participants of the usability study!

13. Thank you for your attention! Question?

14. Backup Slides

15. Usability Study • 9 participants (5 males, 4 females, mean age 26.7, std 2.5) • Background review, Training session, Usability testing • Search for event sequence and replace • Search for people who had both book and newspaper publications where order is not important • Search for people who have at least 10 conference publications • Search and replace all publication events Very noticeable reduction in visual clutter after using Tool Repetition more intuitive than No Order and Wildcard searches

16. Recommended Improvements