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

Overview Motivation Problem Statement Computational Challenges Related Works Approach Examples Conclusion

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

SASB Problem Statement

Definitions Constant Area Buffers –Node buffers –Path buffers

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

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

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

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

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

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

Execution Trace: Final Solution

Best Case Scenario

Better

Average Case Scenario

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

Acknowledgements CSci 8715 peer reviewers who gave valuable suggestions.

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