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Dataset for Qualitative investigation of data-driven decision making within a control room for disaster monitoring and early-warning in Brazil.

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dc.contributor.author Flavio Eduardo Aoki Horita
dc.date 2018-08-23
dc.date.accessioned 2018-08-23
dc.date.accessioned 2019-08-01T17:37:01Z
dc.date.available 2019-08-01T17:37:01Z
dc.identifier.uri http://dx.doi.org/10.5072/FK2/JQYQKQ
dc.identifier.uri https://dataverse.ufabc.edu.br/dataset.xhtml?persistentId=doi:10.5072/FK2/JQYQKQ
dc.description With the emergence of big data and new data sources, a challenge posed to today's organizations consists of identifying how to align their decision-making and organizational processes to data that could help them make better-informed decisions. This paper presents a study in the context of disaster management in Brazil that applies oDMN +, a framework that connects decision-making with data sources through an extended modeling notation and a modeling process. The study results revealed that the framework is an effective approach for improving the understanding of how to leverage big data in the organization's decision-making.
dc.description.sponsorship CNPq
dc.description.sponsorship CAPES
dc.format text/plain
dc.publisher Flavio Eduardo Aoki Horita
dc.subject Computer and Information Science
dc.subject Earth and Environmental Sciences
dc.title Dataset for Qualitative investigation of data-driven decision making within a control room for disaster monitoring and early-warning in Brazil.


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