efficient data modeling and querying system for multi-dimensional spatial data
Multi-dimensional spatial data are obtained when a number of data acquisition devices are deployed at different locations to measure a certain set of attributes of the study subject. How to manipulate these spatial data remains a challenge to the database community, especially when the spatial locations are represented in 3D. In this work, we establish a data model to handle multi-dimensional spatial data with three spatial dimensions. In particular, firstly, a clustering algorithm is applied to group the data set into "point clouds". Secondly, each cloud is considered as a 3D spatial convex object and triangulated into a set of tetrahedrons. Thirdly, all tetrahedron sets are stored in the database and spatial analysis is performed. In this paper, we focus on defining 3D spatial operations and relationships for 3D spatial elements (points, segments, triangles and tetrahedrons), and further applying these operations on 3D spatial objects, where each object is composed of a set of tetrahedrons.
data modeling 3d spatial data multi-dimensions