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: S patial A ctivity S ummarization using B uffers

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Sasb s patial a ctivity s ummarization using b uffers

SASB: Spatial Activity Summarization using Buffers

Atanu Roy & AkashAgrawal


Overview

Overview

  • Motivation

  • Problem Statement

  • Computational Challenges

  • Related Works

  • Approach

  • Examples

  • Conclusion


Motivation

SASB

Motivation

  • 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


Running example

Running Example


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


Related works

Related Works


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


Execution trace

Execution Trace


Execution trace final solution

Execution Trace: Final Solution


Best case scenario

Best Case Scenario


Better

Better


Average case scenario

Average Case Scenario


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.


Thank you

Thank you


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