Cloud Computing Model for Big Geological Data Processing

机译：大地质数据处理云计算模型

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Geological data with phyletic and various, huge and complex data format, the analysis of geological data processing is mainly divided into three parts: Mines forecast, mine evaluation and mine positioning. Traditional geological data analysis model is limited by limited storage space and computational efficiency, and cannot meet the needs of a large number of geological data fast operations. "Big data technology" provides the ideal solution to the vast amounts of geological data management, information extraction, and comprehensive analysis. For mass storage capacity and high-speed computing power that the "big data technology" need, we built an intelligence systems applied to the analysis of geological data based on MapReduce and GPU double parallel processing cloud computing model. For a large number of geological data, using hadoop cluster system to solve the problem of large amounts of data storage, and designing efficient parallel processing method based on GPU (Graphics Processing Units: calculation of Graphics Processing unit), the method was applied to MapReduce framework, finally completing MapReduce and GPU double parallel processing cloud computing model to improve the operation speed of the system. Through theoretical modeling and experimental verification, indicating that the system can meet the analysis of geological data operation precision, the operation data amount and the operation speed.

机译：地质数据具有文学和各种，巨大和复杂的数据格式，地质数据处理的分析主要分为三个部分：矿山预测，矿山评价和矿井定位。传统地质数据分析模型受限的存储空间和计算效率有限，并且无法满足大量地质数据快速操作的需求。 “大数据技术”为广大地质数据管理，信息提取和综合分析提供了理想的解决方案。对于“大数据技术”需要的大容量存储容量和高速计算能力，我们建立了一个智能系统，应用于基于MapReduce和GPU双行处理云计算模型的地质数据分析。对于大量地质数据，使用Hadoop集群系统解决大量数据存储的问题，以及基于GPU的高效并行处理方法（图形处理单元：图形处理单元的计算），该方法应用于MapReduce框架，最后完成MapReduce和GPU双行处理云计算模型，提高了系统的操作速度。通过理论建模和实验验证，表明系统可以满足地质数据操作精度的分析，操作数据量和操作速度。

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《International Conference on Sensors, Measurement and Intelligent Materials》|2014年||共6页
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作者
Miaomiao Song; Zhe Li; Bin Zhou; Chaoling Li;
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中图分类 TP212-532;
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geological data analysis; mining area resource assessment; big data; cloud computing intelligence system; hadoop MapReduce; GPU parallel computing.;

机译：地质数据分析;采矿区资源评估;大数据;云计算智能系统;Hadoop MapReduce;GPU并行计算。;

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4. Cloud Computing Model for Big Geological Data Processing [C] . Miaomiao Song, Zhe Li, Bin Zhou, International Conference on Sensors, Measurement and Intelligent Materials . 2014

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Cloud Computing Model for Big Geological Data Processing

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