Big Data for the Environment and Medicine

Millions of people carry environmental sensors with them, for example to measure temperature, humidity, or air pressure. Multisensor systems monitor potentially dangerous changes in the environment. Current visions of the future suggest that we will increasingly be able to interpret, evaluate, and clearly communicate environmental changes in near real time. Ideally, this will lead to a sustainable improvement or safeguarding of quality of life, for example in the areas of health, environmental protection, or disaster prevention. An interdisciplinary research team at Mainz University of Applied Sciences is dedicated to these goals and is utilizing so-called big data methods, as they are currently emerging in a world of rapidly growing and increasingly heterogeneous mass data. Working closely together, the Geoinformatics program in the School of Engineering, in collaboration with i3mainz, the Institute for Spatial Information and Measurement Technology, and the Big Data Analytics division in the School of Business are further developing promising methods for monitoring natural and human-induced environmental changes. The project investigates the potential of current data mining and machine learning methods for spatiotemporal problems. By developing a meta-learning system—that is, combining predictions from multiple models with novel visualization methods—the project aims to significantly expand the pool of potential users of complex analyses. The goal of the research project is to provide innovative methods that significantly increase the benefits of rapidly growing spatially referenced data sets for the economy and society. For example, a big data analytics system is being developed for problems in the field of smart cities, focusing on the analysis of various sensor data related to environmental and health issues. Furthermore, the degree of autonomy of optical monitoring systems for the precision monitoring of large structures, such as wind turbines or bridges, is being increased through image analysis using deep learning systems; these systems are being tested for reliability and optimized for practical applicability. The applicability of the results is to be ensured through the provision of prototypes.   Minister President Malu Dreyer learns about the “Guided Machine Learning for Predictive Maintenance” collaboration project between CubeServ GmbH and the Department of Economics at Mainz University of Applied Sciences. From left to right: Prof. Dr. Gunther Piller (Department of Economics), Jan Wiesemann (CubeServ), Minister President Malu Dreyer, and Matthias Scholz (Department of Economics) (Photo: Susanne Reiß, Mainz University of Applied Sciences)