Big Data Challenges and Hazards Modeling

Kristy F. Tiampo, Seth McGinnis, Yelena Kropivnitskaya, Jinhui Qin, Michael A. Bauer

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

7 Scopus citations

Abstract

In this work we present an overview of the challenges presented by remote sensing and other big data sources for hazards modeling and response in the world today. Big data not only provides vital information for rapid and efficient assessment of the effects and impacts of natural and anthropogenic effects, but is also an important boundary object facilitating communication and interaction between the relevant scientific, business, and governmental organizations. To effectively serve that role, big data must be credible, salient, and legitimate. The characteristics of big data are examined and we conclude that the most important ones for this application are volume, velocity, variety, and value. We present two different applications from the fields of climate and the solid earth science that are designed to solve these challenges for big data science.

Original languageEnglish
Title of host publicationRisk Modeling for Hazards and Disasters
PublisherElsevier
Pages193-210
Number of pages18
ISBN (Electronic)9780128040713
ISBN (Print)9780128040935
DOIs
StatePublished - Jan 1 2017

Keywords

  • Big data
  • Data assimilation
  • Hazards
  • Pipelining

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