D. Ivanova, S. Zahov. Big Data Analytics of Ocean Water Masses in Internet of Things Ecosystem

Key Words: Internet of Things (IoT); big ocean data; machine learning; linear regression; SVM; Apache Spark; result analysis.

Abstract. The scientific paper has presented the various methods for collecting ocean data in Internet of Things Ecosystem. Most of the big ocean data is associated with sea surface temperature, water flows, air mass movement and their ocean-atmosphere interaction, sea level, sea-ice concentration, ocean topography and their impact on meteorological conditions. All these features of ocean data are of great importance and impact on climate change and its impact on human life. This paper is proposed a method for big data analytics and knowledge discovery of ocean water masses based on machine learning. The experimental framework is based on the Apache Spark environment and uses a PYTHON programming language optimized for big data processing. The experimental investigations have been performed using machine learning algorithms: linear regression and supporting vector machines. The paper has been presented the obtained results and their analysis.