Combined multi-layered big data and responsible AI techniques for enhanced decision support in Shipping
Spandonidis C. Christos, Theodoropoulos Panagiotis, Christos Giordamlis
Abstract
The Shipping Industry has recently begun to experience a rapid change in the way data from ships is collected and processed. Satellite communications, telemetry, data collection and data analytics are some of the contemporary technologies employed that enable rapid and efficient data acquisition and processing, making fleet-wise remote monitoring possible. The key technological challenges to progress to the Internet of Ships framework are the demands on infrastructure and humans as well as on advanced analytics, Deep learning techniques. The main goal of our work is to introduce an innovative platform to harmonize, through big data technologies, data collected from various sensors onboard and to implement extreme scale processing techniques, in order to perform Operational efficiency and Performance optimization. The platform is further benchmarked on a series of pilot demonstrations regarding Fuel Oil Consumption prediction.