地质数据的大数据特性研究.pdf针对于地质大数据研究的材料，主要介绍了关于地质大数据的特性研究。2.

文件名称: 地质数据的大数据特性研究.pdf

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详细说明：针对于地质大数据研究的材料，主要介绍了关于地质大数据的特性研究。2.2 》,2002 《 2016 2.3 23.1 IDO 4V” IT META (freshness (3Vs)L9, Gartner 10-11 “3Vs” McKinsey NIST 3.2.1 21994-2018ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net 26 GIS 3.2.2 (im gray (The Fourth Paradigm )uI3I 3.2.3 1000 45km0.8m 12 km 600( GFS 15J 21994-2018ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net (key-value Bigtable 18 Mongo DElla NO SQL [1 Tom Kalil. Big Data is a Big Deal[R]. 2012. ,2016.23(1):1-9. 2(16,3(3):I [4 Agrawal D, Bernstein P, Bertino E, et al. Challenges and opportunities with big data a community white paper developed by leading researchers across the LM. Computing Research Association, 201 neans 5 Fisher D, De Line R, Czerwinski M, et al. Interactions with [19] big data analytics[J]. Interactions.2012.19: 2 LJ gle rege 2015,45(1):1-44. orm 2015,34(7):1288-1299 8 Tatbul N. Sireatlling data integration: Chall opportunities C]. Procccdings of the 25th Internationa n Data Engineering Workshops, California, 201 155-158 [9 Manyika J, Chui M, Brown B, et al. Big data: the next frontier for innovation, competition, and productivity M]. Mc Kinsey Global Institute, 2011 L10 Zikopoulos P Eaton C. Underst big data: analytics Ior cntcrprisc class hadocp and streaming data[ M. New York Mc Graw-Hil Osborne edia 2011 [11] Meijer E. The world according to LINQJ. Commun ACM 22 [12 Cooper M, Mell P. Tackling Big Datal R]. NIST, 2012 [13 Tony Hey, Stewart Tansley, Kristin Tolle( Editors).The Fourth Paradigm: Data-Intensive Scientific Discovery[r] Microsoft. 2009 ,2012(6):647-6 L15 Ghemawat S. Gobioff H. Leung ST. The Gongle file syster In: Procccdings of the ninctccnth ACM symposium on iples [ m. New York, NY. USA [16 Chang F, Dear J, Ghemawat S, et al. Bigtable A distributed rans 84) 21994-2018ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net 4 26 2014,33:191-196. [2 Guo H. Wang L Chen F, et al. Scientific big data and Digital Earth[J]. Chincsc Sciencc Bulletin, 2014.59(35):5060-5073 [3 Lee J G, Kang M. Gecspatial Big Data: Challenges and Opportunities. big Data Research, 2015, 2(2):74-8 2015,34(7):12601265 [J] [7 Ranina R, Madhavan A, Ng A Y. Large-scale deep unsupervised learning using graphics processors J International Conference on Machine Learning 873-88 [8 Mancgold S, Kersten M. Big Data[J]. ERCIM Ncws, 2012 2015(2)2:151-154. 2013,8(1>:10-1 [12 Fu T C. A review on time series data mining[J]. engineering Applications of Artificial Intelligence, 2011, 24(1): 164-181 2013,50(2):225-239 4) 》《 1995,16(2):182-18 》345 LM」 ,1999 L16」 1989 LJ. L1 GoIzer P. Simon L,, Cato P. el al. Designing Global [19] anufacturing Networks using Big Data[J. Prcccdia Cirp 「J 2015,34():1333-1343 米米半米米米米米米米来米米米米米米米米米米米米米米米米米 [17] Labrinidis A, Jagadish H V. Challenges and opportunities large-scale graph processing[C]. Proceedings of the ACM lig data[j]. Proc: VIDB E: dowimenL, 201 SIGMOD International Conference on Management of Dala Indianapolis, 2010: 35-146 [18 Anderson T W. An Introduction to Multivariate Statistical [22 Laurila J K, Gatica-Perez D, Aad I, et al. The mobile data Analysis. 3rd ed[M. New York: John Wiley & Sons, 200 challenge: big data for mobile computing research. In [19 Wu X, Kumar V, Ross-Quinlan J, et al. Top 10 algorithms in Proceedings of the Workshop on the Nokia Mobile Data data mininglI] Knowl Inf Syst. 2007.14: 1 Challenge [c 1//The 10th International Conference on 20 Dean J, Ghermawat S. Map Reduce: sim plified data prccessing Pervasive Computing, Newcastle, 2012. on large clustersLJJ. Colllllun ACM. 2008. 1: 107-113 211 Maicwicz G, Austcrn M H. Bik A J, ct al. Prcgcl: a systcm for 2013(7):9-10. 21994-2018ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net

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