| Research Abstract |
In the engineering database system, multiple versions of a design including engineering drawings should be managed efficiently. We developed efficient spatial data structures, that are expansions of the R-tree and HR-tree, for version management of engineering drawings.
A novel mechanism to manage the difference between drawings is introduced to the HR-tree to eliminate redundant duplications and to reduce the amount of storage required for the data structure. Data management mechanism and structural properties of our data structure called the MVR+-tree are described. To man age, the moving objects efficiently, the PMD-tree was developed. The PMD-tree is an extension of our data structure called the MD-tree that can access any past states of the data set as well as the present state of the data set. The search efficiencies are almost the same as the MD-tree. For geometric data with a network structure, data compression methods have been proposed. These methods can be applied to the map
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data, power networks, or other graph structures to compress the amount of storage. For an acquisition of 3D geometric data from images, we propose a new range finder method that can obtain the 3D structure and illumination properties at the same time. The method In the engineering database system, multiple versions of a design including engineering drawings should be managed efficiently. We developed efficient spatial data structures, that are expansions of the R-tree and HR-tree, for version management of engineering drawings.
A novel mechanism to manage the difference between drawings is introduced to the HR-tree to eliminate redundant duplications and to reduce the amount of storage required for the data structure. Data management mechanism and structural properties of our data structure called the MVR+-tree are described. To man age the moving objects efficiently, the PMD-tree was developed. The PMD-tree is an extension of our data structure called the MD-tree that can access any past states of the data set as well as the present state of the data set. The search efficiencies are almost the same as the MD-tree. For geometric data with a network structure, data compression methods have been proposed. These methods can be applied to the map data, power networks, or other graph structures to compress the amount of st6rage. For an acquisition of 3D geometric data from images, we propose a new range finder method that can obtain the 3D structure and illumination properties at the same time. The method can construct the 3D model of an actual object automatically. Less
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