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

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.