Ocean Hydrological Spatio-Temperal Data Visualization Based on Data Awareness

Authors

Caixia Li, 1. Satellite Marine Tracking and Control Department of China, Jiangyin 214431, China;;
Song Yuan, 2. Dalian Naval Academy, Dalian 116018, China;
Wang Yi, 1. Satellite Marine Tracking and Control Department of China, Jiangyin 214431, China;;
Li Zhi, 1. Satellite Marine Tracking and Control Department of China, Jiangyin 214431, China;;
Xiaolin Cheng, 1. Satellite Marine Tracking and Control Department of China, Jiangyin 214431, China;;

Abstract

Abstract: Spatio-temporal data mining has emerged as an active research field focusing on the modeling and visualization of ocean hydrological data. A model of spatio-temporal data awareness (SDAM) was proposed, which gave details from three parts: 1) Description with data semantic tag for spatio-temporal context and value. 2) Awareness of spatial neighborhood semantic features by means of tri-linear interpolation; Awareness of temporal neighborhood semantic features by means of spatio-temporal frequent pattern mining; Developing database with the spatio-temporal coupling characteristics. 3) Developing feature cluster and application dataset by spatio-temporal clustering. With an application case of China's hydrological database, SDAM visualization on Web3D was demonstrated. Experimental result shows the effectiveness of the method.

Recommended Citation

Li, Caixia; Yuan, Song; Yi, Wang; Zhi, Li; and Cheng, Xiaolin (2020) "Ocean Hydrological Spatio-Temperal Data Visualization Based on Data Awareness," Journal of System Simulation: Vol. 27: Iss. 9, Article 29.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss9/29

First Page

2150

Revised Date

2015-08-04

DOI Link

https://doi.org/

Last Page

2155

CLC

TP391;P7

Recommended Citation

Li Caixia, Song Yuan, Wang Yi, Li Zhi, Cheng Xiaolin. Ocean Hydrological Spatio-Temperal Data Visualization Based on Data Awareness[J]. Journal of System Simulation, 2015, 27(9): 2150-2155.