A data-driven global observatory addressing worldwide challenges through text mining and complex data visualisation [version 2; peer review: 1 approved, 2 approved with reservations]

Abstract

Observing the world on a global scale can help us understand better the context of problems that engage us all. In this paper, we propose a data-driven global observatory methodology that puts together the different perspectives of media, science, statistics and sensing over heterogeneous data sources and text mining algorithms. We also discuss the implementation of this global observatory in the context of epidemic intelligence, monitoring the impact of the COVID-19 pandemic, and in the context of climate change, with a specific focus on water resource management. Moreover, we discuss the value of this global solution in local contexts and priorities, based on the exchange with stakeholders in municipalities, utilities and governmental institutions.

Keywords

• Big Data
• Semantic Technologies
• Public Health
• Water Management
• Text Mining
• MeSH Headings
• eng