Subjects: Geosciences >> Space Physics submitted time 2017-01-22
Abstract： With the increase of explored data, in order to improve data display result and efficiency, reasonable data arrangement is needed. In this paper, based on the solar wind data characteristics, the SDOG-R grid which is radial independent division in the adaptive SDOG grid is mainly elaborated. In order to make data applicable to the model, a centroid interpolation algorithm based on three-dimensional spherical coordinate system is proposed to re-sample the original data. The division model not only applies to both regular and irregular sampling data, but also maintains the characteristics of original data to the best and encodes grids with improved CDZ curve. The model not only resolves the problem that grids at the two poles and the center of the sphere are too dense, but also resolves the problem that radial resolution is greater than spherical resolution. Keywords: solar wind; space subdivision; SDOG-R;encoding
Peer Review Status:
Awaiting Review
Subjects: Geosciences >> Space Physics submitted time 2017-01-22
Abstract： With the massive increase of space exploration data, in order to improve the visibility effect and data access efficiency, effective organization and management is needed. In this paper, based on the existing 2D subdivision models, combined with the solar wind data characteristics, for the ecliptic plane and meridian plane which are over the sun centroid, a new model, applicable to solar wind data, is put forward, PDQG, namely plane degradation quad-tree grid. Based on improved degradation Z curve Grids, encoding scheme of grids is given. The model not only resolves the problem that grids near to heliocentric are too dense, but also meets the requirement that resolution in radius is not equal to that in longitude and latitude, provides hierarchical multi-resolution data. It can support the organization and management of massive space data effectively.
Peer Review Status:Awaiting Review
Subjects: Geosciences >> Space Physics submitted time 2016-05-13
Abstract： With the quick increase of space data, in order to meet the higher demand of data visualization and data access efficiency, reasonable data organization and management is needed. Based on the existing 2D subdivision models, for the ecliptic plane and meridian plane which are over the Sun centroid, a new model, PDQG-R (Plane Degradation Quad-Tree Grid), is put forward. Encoding scheme of grids is also given. Taken solar wind data as an example, the model not only resolves the problem that grids near to heliocentric are too dense, but also meets the requirement that resolution in radius is not equal to that in longitude and latitude. Besides, it provides hierarchical multi-resolution data, and can support the organization and management of massive space data effectively.
Peer Review Status:Awaiting Review
Subjects: Geosciences >> Space Physics submitted time 2016-04-22
Abstract： With the ever-increasing of space data, a reasonable data-arrangement is strongly required for high efficiency in data accessing and visualization. In this paper, basing on the characteristics of solar-wind data, a SDOG-R grid model is employed, which is radial independent division in the adaptive SDOG grid. For huge Sun-Earth space, it is divided into multi-resolution grids. The original data, which is output from a SIP-CESE solar wind model, are re-sampled, and put into the subdivided grids. The grid model is applied to both regular and irregular sampling data, and fully maintains the characteristics of original data to the best and encodes grids with an improved CDZ curve. Specific examples of data organization are given. The experiments prove that the grid model not only resolves the problem that grids are too dense at two poles and the spherical center, but also meets the need for higher resolution in radius than in latitude and longitude. Besides, 3D LOD spatial data model can not only provide multi-resolution data, but also significantly improve the large-scale mass data retrieval and access efficiency, and can support the organization and management of massive spatial data effectively.
Peer Review Status:Awaiting Review
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