Mining Information Science and Big Data Concept for Integrated Safety Monitoring in Subsoil Management
I. V. BYCHKOV1, D. YA. VLADIMIROV2, V. N. OPARIN3, V. P. POTAPOV4, YU. I. SHOKIN5
1Institute for System Dynamics and Control Theory, Siberian Branch, Russian Academy of Sciences, ul. Lermontova 134, Irkutsk, 664033 Russia
2VIST Group, Dokuchaev per. 3, Bld. 1, Moscow, 107078 Russia
3Chinakal Institute of Mining, Siberian Branch, Russian Academy of Sciences, Krasnyi pr. 54, Novosibirsk, 630091 Russia
4Institute of Computational Technologies, Siberian Branch, Russian Academy of Sciences, ul. Rukavishnikova 21, Kemerovo, 650025 Russia
5Institute of Computational Technologies, Siberian Branch, Russian Academy of Sciences, pr. Akademika Lavrentieva 6, Novosibirsk, 630090 Russia
Keywords: "большие данные", интеллектуальный анализ, вычислительные и мини-кластеры, неструктурированные массивы информации, потоковая обработка геомеханических и геодинамических данных, облачные технологии, распределенные вычисления, безопасное недропользование, Big Data, intelligent analysis, computational and mini-clusters, raw data sets, geomechanical and geodynamic data flow computing, cloud computing, distributed computing, safe subsoil management
Subsection: GEOINFORMATION SCIENCE
Abstract
The discussed challenge and its prospects in mining geoinformation science are connected with Big Data concept-flows of large sets of various data on mining. The authors describe Big Data technology and its general implementation on mini-clusters using Hadoop and MapReduce with case studies presented.
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