Victor Marchezini

Bridging the gap between decision-making and emerging big data sources

With the emergence of big data and new data sources, a challenge posed to todays 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 organizations decision-making. An extended model-based framework connects decision-making to big data sources.A better understanding of decision-making is achieved with the framework.A modeling process is outlined for systematically using the framework in practice.Decision-making can be improved through the use of standard models and notations.Lessons were learned from a case study on a Brazilian disaster early-warning center.

A review of studies on Participatory Early Warning Systems (P-EWS): pathways to support citizen science initiatives

Context: Global environmental change and disasters pose several challenges to governments, society and science. These challenges occurred in social contexts were information and communication technologies can be used to share data and information, engaging citizen scientists in multidirectional and decentralized knowledge creation initiatives. Often referenced as participatory (or people-centered) early warning systems, this has been of a great potential to improve decisions taken by both emergency institutions and exposed and/or affected communities. Several methodologies have been proposed, mainly in natural science, redefining traditional ways of transferring knowledge about scientific process to the public. Gap: However, practice and research still lack studies that investigate how citizens can be involved in citizen science to support early warning systems. From a social science perspective, this is important as these works do not fill the gap between citizen science and disaster prevention. While, on a technological and perspective, efforts have been concentrated on developing systems, methodologies, and approaches rather than understanding citizens’ requirements or ways of better engaging citizens. Obj: This paper provides a social science framework to determine the elements of how citizen science and participatory early warning systems can be bridged. Method: For doing so, we will conduct a systematic mapping for examining the literature on citizen science and disaster management, in particular, those focused on social science and participatory approaches for early warning systems. Results of this review showed that only 3,35% (14 of 417) articles were related to citizen science and P-EWS, which indeed indicate that much effort is needed to disseminate what is citizen science and how it can be mainstreamed in DRM field. Furthermore, the proposed framework can contribute by enhancing stakeholders’ reflexivity about EWS.