Extracting Places from Traces of Locations

1. Extracting Places from Traces of Locations Paper Authors Jong Hee Kang Benjamin Stewart William Welbourne Gaetano Borriello PowerPoint Author Michael Cook

2. Michael Cook • 4th Year Computer Science (Junior) • Co-oping at Synovus • Interests • Databases • Networking • Web Development • Twin brother

3. The Problem • Location aware systems today are limiting • Place: An area of importance to a user • Usage Example: • Cell phone goes to “silent” mode when entering a classroom

4. Ideal Situation • Requires little user interaction • All important places are located • No false positives • Works for indoor and outdoor places

5. Tracking User Movement • Place Lab access points • Works indoors

6. Popular Clustering Algorithms K-means Gaussian mixture model Large amounts of computation

7. Time-Based Clustering • Streaming computation • Small clusters ignored • Time threshold and distance threshold can be changed

8. Time-Based Clustering Result

9. Changing Distance and Time

10. Changing Distance and Time d=30m t=300sec d=50m t=300sec d=300m t=600sec

11. Frequently Visited Places • Not much time is spent at the place, but frequently visited • Different time threshold needed • How to differentiate the place and in-transit motion?

12. Future Work • Automatic labeling of places • Can use user’s calendar • Learn proper distance and time thresholds automatically

13. Easy to read and understand Cool idea with practical applications WiFi hotspots not always available Trying to do too much at once Long duration places Short duration, frequent places Critique

14. Questions?