Author List: Chen, Rui; Sharman, Raj; Rao, H. Raghav; Shambhu, Upadhyaya J.;
MIS Quarterly, 2013, Volume 37, Issue 1, Page 125-147.
Post-analyses of major extreme events reveal that information sharing is critical for effective emergency response. The lack of consistent data standards for current emergency management practice, however, hinders efficient critical information flow among incident responders. In this paper, we adopt a third-generation activity theory guided approach to develop a data model that can be used in the response to fire-related extreme events. This data model prescribes the core data standards to reduce information inter operability barriers. The model is validated through a three-step approach including a request for comment (RFC) process, case application, and prototype system test. This study contributes to the literature in the area of interoperability and data modeling; it also informs practice in emergency response system design.
Keywords: Data model; extreme events
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#163 0.128 critical realism theory case study context affordances activity causal key identifies evolutionary history generative paper events lead mechanisms evolution change
#287 0.127 design systems support development information proposed approach tools using engineering current described developing prototype flexible built architecture environment integrated designing
#127 0.125 systems information research theory implications practice discussed findings field paper practitioners role general important key grounded researchers domain new identified
#191 0.111 model models process analysis paper management support used environment decision provides based develop use using help literature mathematical presented formulation
#133 0.103 data predictive analytics sharing big using modeling set power inference behavior explanatory related prediction statistical generated substantially novel building million
#6 0.096 data used develop multiple approaches collection based research classes aspect single literature profiles means crowd collected trend accuracy databases accurate