This study analyzes and summarizes seven main characteristics of the marine data sampled by multiple underwater gliders. These characteristics such as the big data volume and data sparseness make it extremely difficult to do some meaningful applications like early warning of marine environment. In order to make full use of the sea trial data, this paper gives the definition of two types of marine data cube which can integrate the big marine data sampled by multiple underwater gliders along saw-tooth paths, and proposes a data fitting algorithm based on time extraction and space compression (DFTS) to construct the temperature and conductivity data cubes. This research also presents an early warning algorithm based on data cube (EWDC) to realize the early warning of a new sampled data file. Experiments results show that the proposed methods are reasonable and effective. Our work is the first study to do some realistic applications on the data sampled by multiple underwater vehicles, and it provides a research framework for processing and analyzing the big marine data oriented to the applications of underwater gliders.
China Ocean Engineering
the National Natural Science Foundation of China (Grant Nos. U1709202 and No. 61502069)
the Foundation of State Key Laboratory of Robotics (Grant No. 2015-o03)
the Fundamental Research Funds for the Central Universities (Grant Nos. DUT18JC39 and DUT17JC45).
Corresponding author:YU Jian-cheng, E-mail: email@example.com.