SASB: S patial A ctivity S ummarization using B uffers

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SASB: S patial A ctivity S ummarization using B uffers. 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.

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### SASB: Spatial Activity Summarization using Buffers

Atanu Roy & AkashAgrawal

Overview
• Motivation
• Problem Statement
• Computational Challenges
• Related Works
• Approach
• Examples
• Conclusion
SASBMotivation
• Applications in domains like
• Public safety
• Disaster relief operations
SASB Problem Statement
• Input
• A spatial network,
• Set of activities & their location in space,
• Number of buffers required (k),
• A set of buffer (β),
• Output
• A set of k active buffers, where
• Objective
• Maximize the number of activities covered in the kbuffers
• Constraints
• Minimize computation costs
Definitions
• Constant Area Buffers
• Node buffers
• Path buffers
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
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
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.