1. SASB: Spatial Activity Summarization using Buffers Atanu Roy & Akash Agrawal

2. Overview Motivation Problem Statement Computational Challenges Related Works Approach Examples Conclusion

3. Motivation Applications in domains like –Public safety –Disaster relief operations SASB

4. SASB Problem Statement

5. Definitions Constant Area Buffers –Node buffers –Path buffers

6. Running Example Coverage Path Buffer = 16Node Buffer = 15Total Coverage = 31/33

7. Computational Challenges SASB is NP-Hard Proof: –KMR is a special case of SASB Buffers have width = 0 –KMR is proved to be NP-Complete –SASB is at least NP-Hard

8. Related Works Geometry based NoYes Network based Yes Path based: KMR, Mean Streets 0-1 Subgraph: SANET, Max Subgraph This work No - K-Means, K-Medoids, P-median, Hierarchical Clustering

9. Contributions Definition SASB problem NP-Hardness proof Combination of geometry and network based summarization. First principle examples

10. Greedy Approach Choice of k-best buffers Repeat k times –Choose the buffer with maximum activities –Delete all activities contained in the chosen buffer from all the remaining buffers –Replace the chosen buffer from buffer pool to the result-set

11. Execution Trace NB_A = 8 NB_B = 6 NB_C = 11 PB_1 = 8 PB_2 = 12 PB_12 = 2 NB_A = 8 NB_B = 6NB_B = 2 NB_C = 11NB_C = 1 PB_1 = 8PB_1 = 7 PB_2 = 12PB_2 = NA PB_12 = 2PB_12 = 1

12. Execution Trace: Final Solution

13. Best Case Scenario

14. Better

15. Average Case Scenario

16. Conclusion Provides a framework to fuse geometry and network based approaches. First principle examples indicates it can be comparable with related approaches.

17. Acknowledgements CSci 8715 peer reviewers who gave valuable suggestions.

18. Thank you