Ocean Hydrological Spatio-Temperal Data Visualization Based on Data Awareness

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.

Key words: ocean hydrological data, AM, spatio-temporal clustering, WebGL, 3D visualization

CLC Number:

TP391
P7

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.

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