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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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Presentation Transcript
overview
Overview
  • Motivation
  • Problem Statement
  • Computational Challenges
  • Related Works
  • Approach
  • Examples
  • Conclusion
motivation
SASBMotivation
  • Applications in domains like
    • Public safety
    • Disaster relief operations
sasb problem statement
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
Definitions
  • Constant Area Buffers
    • Node buffers
    • Path buffers
computational challenges
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
Contributions
  • Definition SASB problem
  • NP-Hardness proof
  • Combination of geometry and network based summarization.
  • First principle examples
greedy approach
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
Conclusion
  • Provides a framework to fuse geometry and network based approaches.
  • First principle examples indicates it can be comparable with related approaches.
acknowledgements
Acknowledgements
  • CSci 8715 peer reviewers who gave valuable suggestions.
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