Data extrapolation in social sensing for disaster response

Siyu Gu, Chenji Pan, Hengchang Liu, Shen Li, Shaohan Hu, Lu Su, Shiguang Wang, Dong Wang, Tanvir Amin, Ramesh Govindan, Charu Aggarwal, Raghu Ganti, Mudhakar Srivatsa, Amotz Barnoy, Peter Terlecky, Tarek Abdelzaher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that 'fill-in' missing pieces. Unlike trend-extrapolation methods, we focus on prediction in disaster scenarios where disruptive trend changes occur. A set of prediction heuristics (and a standard trend extrapolation algorithm) are compared that use either predominantly-spatial or predominantly-temporal correlations for data extrapolation purposes. The evaluation shows that none of them do well consistently. This is because monitored system state, in the aftermath of disasters, alternates between periods of relative calm and periods of disruptive change (e.g., aftershocks). A good prediction algorithm, therefore, needs to intelligently combine time-based data extrapolation during periods of calm, and spatial data extrapolation during periods of change. The paper develops such an algorithm. The algorithm is tested using data collected during the New York City crisis in the aftermath of Hurricane Sandy in November 2012. Results show that consistently good predictions are achieved. The work is unique in addressing the bi-modal nature of damage propagation in complex systems subjected to stress, and offers a simple solution to the problem.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2014
PublisherIEEE Computer Society
Pages119-126
Number of pages8
ISBN (Print)9781479946198
DOIs
StatePublished - 2014
Event9th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2014 - Marina Del Rey, CA, United States
Duration: May 26 2014May 28 2014

Publication series

NameProceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2014

Other

Other9th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2014
CountryUnited States
CityMarina Del Rey, CA
Period5/26/145/28/14

Keywords

  • data extrapolation
  • disaster response
  • social sensing

ASJC Scopus subject areas

  • Software

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