Chong-chong Qi, Big data management in the mining industry, Int. J. Miner. Metall. Mater., 27(2020), No. 2, pp. 131-139. https://doi.org/10.1007/s12613-019-1937-z
Cite this article as:
Chong-chong Qi, Big data management in the mining industry, Int. J. Miner. Metall. Mater., 27(2020), No. 2, pp. 131-139. https://doi.org/10.1007/s12613-019-1937-z
Invited Review

Big data management in the mining industry

+ Author Affiliations
  • Corresponding author:

    Chong-chong Qi    E-mail: 21948042@student.uwa.edu.au

  • Received: 18 September 2019Revised: 16 December 2019Accepted: 20 December 2019Available online: 25 December 2019
  • The mining industry faces a number of challenges that promote the adoption of new technologies. Big data, which is driven by the accelerating progress of information and communication technology, is one of the promising technologies that can reshape the entire mining landscape. Despite numerous attempts to apply big data in the mining industry, fundamental problems of big data, especially big data management (BDM), in the mining industry persist. This paper aims to fill the gap by presenting the basics of BDM. This work provides a brief introduction to big data and BDM, and it discusses the challenges encountered by the mining industry to indicate the necessity of implementing big data. It also summarizes data sources in the mining industry and presents the potential benefits of big data to the mining industry. This work also envisions a future in which a global database project is established and big data is used together with other technologies (i.e., automation), supported by government policies and following international standards. This paper also outlines the precautions for the utilization of BDM in the mining industry.
  • loading
  • [1]
    A.G.N. Kitula, The environmental and socio-economic impacts of mining on local livelihoods in Tanzania: A case study of Geita District, J. Cleaner Prod., 14(2006), No. 3-4, p. 405. doi: 10.1016/j.jclepro.2004.01.012
    [2]
    D. Ma, J.J. Wang, and Z.H. Li, Effect of particle erosion on mining-induced water inrush hazard of karst collapse pillar, Environ. Sci. Pollut. Res., 26(2019), No. 19, p. 19719. doi: 10.1007/s11356-019-05311-x
    [3]
    K. Peng, J.Q. Zhou, Q.L. Zou, J. Zhang, and F. Wu, Effects of stress lower limit during cyclic loading and unloading on deformation characteristics of sandstones, Constr. Build. Mater., 217(2019), p. 202. doi: 10.1016/j.conbuildmat.2019.04.183
    [4]
    S.H. Yin, Y.J. Shao, A.X. Wu, H.J. Wang, X.H. Liu, and Y. Wang, A systematic review of paste technology in metal mines for cleaner production in China, J. Cleaner Prod., 247(2020), art. No. 119590.
    [5]
    H.Z. Jiao, S.F. Wang, A.X. Wu, H.M. Shen, and J.D. Wang, Cementitious property of NaAlO2-activated Ge slag as cement supplement, Int. J. Miner. Metall. Mater., 26(2019), No. 12, p. 1594. doi: 10.1007/s12613-019-1901-y
    [6]
    Y.Y. Tan, X. Yu, D. Elmo, L.H. Xu, and W.D. Song, Experimental study on dynamic mechanical property of cemented tailings backfill under SHPB impact loading, Int. J. Miner. Metall. Mater., 26(2019), No. 4, p. 404. doi: 10.1007/s12613-019-1749-1
    [7]
    C.C. Qi and A. Fourie, Cemented paste backfill for mineral tailings management: Review and future perspectives, Miner. Eng., 144(2019), art. No. 106025.
    [8]
    A. Azapagic, Developing a framework for sustainable development indicators for the mining and minerals industry, J. Cleaner Prod., 12(2004), No. 6, p. 639. doi: 10.1016/S0959-6526(03)00075-1
    [9]
    J.A. Fekete, Big Data in Mining Operations [Dissertation], University of Copenhagen, Denmark, 2015, p. 71.
    [10]
    Y.J. Shen, Y.Z. Wang, Y. Yang, Q. Sun, T. Luo, and H. Zhang, Influence of surface roughness and hydrophilicity on bonding strength of concrete-rock interface, Constr. Build. Mater., 213(2019), p. 156. doi: 10.1016/j.conbuildmat.2019.04.078
    [11]
    V. Mayer-Schönberger and K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt, 2013.
    [12]
    K.C. Ho, L.M. Collins, L.G. Huettel, and P.D. Gader, Discrimination mode processing for EMI and GPR sensors for hand-held land mine detection, IEEE Trans. Geosci. Remote Sens., 42(2004), No. 1, p. 249. doi: 10.1109/TGRS.2003.817804
    [13]
    C.C. Qi, A. Fourie, and Q.S. Chen, Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill, Constr. Build. Mater., 159(2018), p. 473. doi: 10.1016/j.conbuildmat.2017.11.006
    [14]
    N. Attoh-Okine, Big data challenges in railway engineering, [in] 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, 2014, p. 7.
    [15]
    J.S. Ward and A. Barker, Undefined by data: a survey of big data definitions, arXiv preprint arXiv, 2013, art. No. 1309.5821.
    [16]
    M. Bilal, L.O. Oyedele, J. Qadir, K. Munir, S.O. Ajayi, O.O. Akinade, H.A. Owolabi, H.A. Alaka, and M. Pasha, Big Data in the construction industry: A review of present status, opportunities, and future trends, Adv. Eng. Inf., 30(2016), No. 3, p. 500. doi: 10.1016/j.aei.2016.07.001
    [17]
    O. Kapliński, N. Košeleva, and G. Ropaitė, Big Data in civil engineering: A state-of-the-art survey, Eng. Struct. Technol., 8(2016), No. 4, p. 165. doi: 10.3846/2029882X.2016.1257373
    [18]
    D. Singh and C.K. Reddy, A survey on platforms for big data analytics, J. Big Data, 2(2015), No. 1, p. 8. doi: 10.1186/s40537-014-0008-6
    [19]
    M. Hasanipanah, D.J. Armaghani, M. Monjezi, and S. Shams, Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system, Environ. Earth Sci., 75(2016), No. 9, p. 808. doi: 10.1007/s12665-016-5503-y
    [20]
    D.J. Armaghani, A. Mahdiyar, M. Hasanipanah, R.S. Faradonbeh, M. Khandelwal, and H.B. Amnieh, Risk assessment and prediction of flyrock distance by combined multiple regression analysis and monte carlo simulation of quarry blasting, Rock Mech. Rock Eng., 49(2016), No. 9, p. 3631. doi: 10.1007/s00603-016-1015-z
    [21]
    D.J. Armaghani, E.T. Mohamad, M.S. Narayanasamy, N. Narita, and S. Yagiz, Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition, Tunnelling Underground Space Technol., 63(2017), p. 29. doi: 10.1016/j.tust.2016.12.009
    [22]
    X. Lu, M. Hasanipanah, K. Brindhadevi, H.B. Amnieh, and S. Khalafi, ORELM: A novel machine learning approach for prediction of flyrock in mine blasting, Nat. Resour. Res., 2019, p. 1.
    [23]
    B. King, M. Goycoolea, and A. Newman, Optimizing the open pit-to-underground mining transition, Eur. J. Oper. Res., 257(2017), No. 1, p. 297. doi: 10.1016/j.ejor.2016.07.021
    [24]
    H.J. Lu, C.C. Qi, Q.S. Chen, D.Q. Gan, Z.L. Xue, and Y.J. Hu, A new procedure for recycling waste tailings as cemented paste backfill to underground stopes and open pits, J. Cleaner Prod., 188(2018), p. 601. doi: 10.1016/j.jclepro.2018.04.041
    [25]
    L. Liu, C. Zhu, C.C. Qi, M. Wang, C. Huan, B. Zhang, and K.I. Song, Effects of curing time and ice-to-water ratio on performance of cemented paste backfill containing ice slag, Constr. Build. Mater., 228(2019), art. No. 116639.
    [26]
    R.H.E.M. Koppelaar and H. Koppelaar, The ore grade and depth influence on copper energy inputs, Biophys. Econ. Resour. Qual., 1(2016), No. 2, p. 11. doi: 10.1007/s41247-016-0012-x
    [27]
    Z.Y. Song, D.X. Niu, and X.L. Xiao, Focus on the current competitiveness of coal industry in China: Has the depression time gone?, Resour. Policy, 51(2017), p. 172. doi: 10.1016/j.resourpol.2016.11.011
    [28]
    A. Lane, J. Guzek, and W. Van Antwerpen, Tough choices facing the South African mining industry, J. South Afr. Inst. Min. Metall., 115(2015), No. 6, p. 471.
    [29]
    S. Lozeva and D. Marinova, Negotiating gender: Experience from Western Australian mining industry, J. Econ. Soc. Policy, 13(2010), No. 2, p. 7.
    [30]
    F. Provost and T. Fawcett, Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking, O'Reilly Media, Inc., Sebastopol, 2013.
    [31]
    T. White, Hadoop: The definitive guide, O'Reilly Media, Inc., Sebastopol, 2012.
    [32]
    P. Helland, If you have too much data, then 'good enough' is good enough, Commun. ACM, 54(2011), No. 6, p. 40. doi: 10.1145/1953122.1953140
    [33]
    C.Q. Ji, Y. Li, W.M. Qiu, U. Awada, and K.Q. Li, Big data processing in cloud computing environments, [in] 2012 12th International Symposium on Pervasive Systems, Algorithms and Networks, San Marcos, TX, 2012, p. 17.
    [34]
    S. Lee and Y. Choi, Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry, Geosyst. Eng., 19(2016), No. 4, p. 197. doi: 10.1080/12269328.2016.1162115
    [35]
    P. Baumann, P. Mazzetti, J. Ungar, et al., Big data analytics for earth sciences: the EarthServer approach, Int. J. Digital Earth, 9(2016), No. 1, p. 3. doi: 10.1080/17538947.2014.1003106
    [36]
    S.H. Zhang, K.Y. Xiao, Y.S. Zhu, and N. Cui, A prediction model for important mineral resources in China, Ore Geol. Rev., 91(2017), p. 1094. doi: 10.1016/j.oregeorev.2017.09.010
    [37]
    J. Bughin, M. Chui, and J. Manyika, Clouds, big data, and smart assets: Ten tech-enabled business trends to watch, McKinsey Q., 56(2010), No. 1, p. 75.
    [38]
    J. Ralston, D. Reid, C. Hargrave, and D. Hainsworth, Sensing for advancing mining automation capability: A review of underground automation technology development, Int. J. Min. Sci. Technol., 24(2014), No. 3, p. 305. doi: 10.1016/j.ijmst.2014.03.003
    [39]
    D. Reid and A. Fourie, Geotechnical effects of polymer treatment on tailings–state of knowledge review, [in] Proceedings of the 21st International Seminar on Paste and Thickened Tailings, Perth, 2018, p. 263.
    [40]
    J.C. Bertot and H. Choi, Big data and e-government: issues, policies, and recommendations, [in] Proceedings of the 14th Annual International Conference on Digital Government Research, ACM, Quebec, 2013, p. 1.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)

    Share Article

    Article Metrics

    Article views (1303) PDF downloads(74) Cited by()
    Proportional views

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return