Data organization requires high efficiency for large amount of data applied in the digital mine system. A new method of storing massive data of block model is proposed to meet the characteristics of the database, incl...Data organization requires high efficiency for large amount of data applied in the digital mine system. A new method of storing massive data of block model is proposed to meet the characteristics of the database, including ACID-compliant, concurrency support, data sharing, and efficient access. Each block model is organized by linear octree, stored in LMDB（lightning memory-mapped database）. Geological attribute can be queried at any point of 3D space by comparison algorithm of location code and conversion algorithm from address code of geometry space to location code of storage. The performance and robustness of querying geological attribute at 3D spatial region are enhanced greatly by the transformation from 3D to 2D and the method of 2D grid scanning to screen the inner and outer points. Experimental results showed that this method can access the massive data of block model, meeting the database characteristics. The method with LMDB is at least 3 times faster than that with etree, especially when it is used to read. In addition, the larger the amount of data is processed, the more efficient the method would be.展开更多
Three global datasets, the History Database of the Global Environment （HYDE）, Kaplan and Krurnhardt （KK） and Pongratz of reconstructed anthropogenic land cover change （ALCC） were introduced and compared in this ...Three global datasets, the History Database of the Global Environment （HYDE）, Kaplan and Krurnhardt （KK） and Pongratz of reconstructed anthropogenic land cover change （ALCC） were introduced and compared in this paper. The HYDE dataset was recon- structed by Goldewijk and his colleagues at the National institute of Public ttealth and the Environment in Netherland, covering the past 12 000 years. The KK dataset was reconstructed by Kaplan and his colleagues, the Soil-Vegetation-Atmosphere Research Group at the Institute of Environmental Engineering in Switzerland, covering the past 8000 years. The Pongratz dataset was reconstructed by Pon- gratz and her colleagues at the Max Planck Institute for Meteorology in Germany, coveting AD 800-1992. The results show that the reconstructed datasets are quite different from each other due to the different methods used. The three datasets all allocated the historical ALCC according to human population density. The main reason causing the differences among the three datasets lies on the different relationships between population density and land use used in each reconstructed dataset. The KK dataset is better than the other two datasets for two important reasons. First, it used the nonlinear relationship between population density and land use, while the other two used the linear relationship. Second, Kaplan and his colleagues adopted the technological development and intensification parameters and considered the wood harvesting and the long-term fallow area resulted from shifting cultivation, which were neglected in the recon- structions of the other two datasets. Therefore, the KK dataset is more suitable as one of the anthropogenic forcing fields for climate simulation over the past two millennia that is recently concerned by two projects, the National Basic Research Program and the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences.展开更多
基金Projects(41572317,51374242)supported by the National Natural Science Foundation of ChinaProject(2015CX005)supported by the Innovation Driven Plan of Central South University,China
文摘Data organization requires high efficiency for large amount of data applied in the digital mine system. A new method of storing massive data of block model is proposed to meet the characteristics of the database, including ACID-compliant, concurrency support, data sharing, and efficient access. Each block model is organized by linear octree, stored in LMDB（lightning memory-mapped database）. Geological attribute can be queried at any point of 3D space by comparison algorithm of location code and conversion algorithm from address code of geometry space to location code of storage. The performance and robustness of querying geological attribute at 3D spatial region are enhanced greatly by the transformation from 3D to 2D and the method of 2D grid scanning to screen the inner and outer points. Experimental results showed that this method can access the massive data of block model, meeting the database characteristics. The method with LMDB is at least 3 times faster than that with etree, especially when it is used to read. In addition, the larger the amount of data is processed, the more efficient the method would be.
基金Under the auspices of Strategic and Special Frontier Project of Science and Technology of Chinese Academy of Sciences (No. XDA05080800)National Basic Research Program of China (No. 2010CB950102)National Natural Science Foundation of China (No. 40871007)
文摘Three global datasets, the History Database of the Global Environment （HYDE）, Kaplan and Krurnhardt （KK） and Pongratz of reconstructed anthropogenic land cover change （ALCC） were introduced and compared in this paper. The HYDE dataset was recon- structed by Goldewijk and his colleagues at the National institute of Public ttealth and the Environment in Netherland, covering the past 12 000 years. The KK dataset was reconstructed by Kaplan and his colleagues, the Soil-Vegetation-Atmosphere Research Group at the Institute of Environmental Engineering in Switzerland, covering the past 8000 years. The Pongratz dataset was reconstructed by Pon- gratz and her colleagues at the Max Planck Institute for Meteorology in Germany, coveting AD 800-1992. The results show that the reconstructed datasets are quite different from each other due to the different methods used. The three datasets all allocated the historical ALCC according to human population density. The main reason causing the differences among the three datasets lies on the different relationships between population density and land use used in each reconstructed dataset. The KK dataset is better than the other two datasets for two important reasons. First, it used the nonlinear relationship between population density and land use, while the other two used the linear relationship. Second, Kaplan and his colleagues adopted the technological development and intensification parameters and considered the wood harvesting and the long-term fallow area resulted from shifting cultivation, which were neglected in the recon- structions of the other two datasets. Therefore, the KK dataset is more suitable as one of the anthropogenic forcing fields for climate simulation over the past two millennia that is recently concerned by two projects, the National Basic Research Program and the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences.
