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Industrial Big Data Analytics: Challenges and Opportunities

Abstract

Manufacturing industries generate a large amount of data from various devices, systems and applications. Challenges, including both data management and data analysis exist in Industry 4.0 with few solutions to handle processing large amounts of data. The data needs to be processed, analyzed and secured to help improve the systems efficiency, safety and scalability. Hence, a new approach is needed to support industrial big data analytics. Industry 4.0 is a new advanced manufacturing vision originated by the German government. Since it is a new concept, there are only several existing surveys that discuss the connection between cyber physical systems and industrial big data analytics. Therefore, this survey will present new concepts, methodologies and application scenarios to reach full industrial autonomy and bring more attention to existing challenges between big data analytics and cyber physical systems. Current solutions, implemented through cyber physical systems, are discussed to highlight desired future research directions.

Keywords

  • Big data
  • IT
  • Cyber physical systems
  • IoT
  • Industry 4.0

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References

  1. M. Brettel, N. Friederchsen, M. Keller, M. Rosenberg, How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. World Acad. Sci. Eng. Technol. 8(1), 37–44 (2014)

    Google Scholar 

  2. L. Bassi, in Industry 4.0: Hope, Hype or Revolution? IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), (2017), pp. 1–6

    Google Scholar 

  3. L.D. Xu, L. Duan, Big data for cyber physical systems in industry 4.0: A survey. Enterp. Inf. Syst. 13(2), 148–169 (2019)

    MathSciNet  CrossRef  Google Scholar 

  4. S. Yin, O. Kaynak, Big data for modern industry: Challenges and trends [point of view]. Proc. IEEE 103(2), 143–146 (2015). https://doi.org/10.1109/JPROC.2015.2388958

    CrossRef  Google Scholar 

  5. Y. Lu, Cyber physical system (Cps)-based industry 4.0: A survey. J. Ind. Integr. Manag. 2(3) (2017b). https://doi.org/10.1142/S2424862217500142

  6. Y. Lu, Industry 4.0: A survey on Technologies, applications and open research issues. J. Ind. Inf. Integr. 6, 1–10 (2017). https://doi.org/10.1016/j.jii.2017.04.005

    CrossRef  Google Scholar 

  7. H. Lasi, P. Fettke, G. Kemper, T. Feld, M. Hoffmann, Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014). https://doi.org/10.1007/s12599-014-0334-4

    CrossRef  Google Scholar 

  8. S. Li, L.D. Xu, S. Zhao, 5G internet of things: A survey. J. Ind. Inf. Integr. 10, 1–9 (2018). https://doi.org/10.1016/j.jii.2018.01.005

    CrossRef  Google Scholar 

  9. J. Wang, W. Zhang, Y. Shi, S. Duan, J. Liu, Industrial big data analytics: challenges, methodologies, and applications. IEEE Trans. Automat. Sci. Eng. 1–12 (2018)

    Google Scholar 

  10. S. Ganschar, M. Gerlach, T. Hammerle, S. Krause, in Arbeit der Zukunft – Mensch und. Produktionsarbeit Der Zukunft-Industrie 4.0, 2013, ed. by D. Spath, pp. 50–56

    Google Scholar 

  11. H. Chen, Applications of cyber-physical system: A literature review. J. Ind. Integr. Manag. 2(3), 2424–8622 (2017b). https://doi.org/10.1142/S2424862217500129

    CrossRef  Google Scholar 

  12. H. Chen, Theoretical foundations for cyber-physical systems: A literature review. J. Ind. Integr. Manag. 2(3), 2424–8630 (2017). https://doi.org/10.1142/S2424862217500130

    CrossRef  Google Scholar 

  13. J. Lee, H. Ardakani, S. Yang, B. Bagheri, Industrial big data analytics and cyber-physical Systems for Future Maintenance & service innovation. Proc. CIRP 38, 3–7 (2015). https://doi.org/10.1016/j.procir.2015.08.026

    CrossRef  Google Scholar 

  14. E. Lee, in Cyber Physical Systems: Design Challenges. Object Oriented Real-Time Distributed Computing (ISORC), (2008), pp. 363–369

    Google Scholar 

  15. L. Xu, Editorial: inaugural issue. Enterp. Inf. Syst. 1(1), 1–2 (2007). https://doi.org/10.1080/17517570712331393320

    MathSciNet  CrossRef  Google Scholar 

  16. J. Lee, E. Lapira, B. Bagheri, H. Kao, Recent advances and trends in predictive manufacturing systems in big data environment. Manuf. Lett. 1(1), 38–41 (2013). https://doi.org/10.1016/j.mfglet.2013.09.005

    CrossRef  Google Scholar 

  17. M. Baily, J. Manyka, Is Manufacturing ‘Cool’ Again (McKinsey Global Institute, 2013), Retrieved 18 July 2019

    Google Scholar 

  18. Y. Chen, H. Chen, A. Gorkhali, Y. Lu, Y. Ma, L. Li, Big data analytics and big data science: A survey. J. Manag. Anal. 3(2), 1–42 (2016). https://doi.org/10.1080/23270012.2016.1141332

    CrossRef  Google Scholar 

  19. The rise of industrial big data. (2012). GE Intelligent Platforms

    Google Scholar 

  20. What is Big Data? | Big Data Definition | V’s of Big Data. (2018). Retrieved 7 18, 2019, from https://www.edureka.co/blog/what-is-big-data/

  21. D. Laney, 3-D Data Management: Controlling Data Volume, Velocity and Variety (META Group, 2001). Research Note

    Google Scholar 

  22. A. Mauro, M. Greco, M. Grimaldi, A formal definition of big data based on its essential features. Libr. Rev. 65(3), 122–135 (2016). https://doi.org/10.1108/LR-06-2015-0061

    CrossRef  Google Scholar 

  23. M. Schroeck, R. Shockley, J. Smart, D. Romero-Morrales, P. Tufano, Analytics: The Real-World Use of Big (IBM Global Business Services, 2012). Retrieved from https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=

  24. J. Dijcks, Oracle: Big Data for the Enterprise. Oracle White Paper, (2012), Retrieved from http://www.oracle.com/us/products/

  25. H. Karimipour, A. Rahimnezhad, H. Rouzba, Smart households demand response management with micro grid. arXiv 1, –7 (2019c)

    Google Scholar 

  26. H. Karimipour, V. Dinavahi, Parallel domain decomposition based distributed state estimation for large-scale power systems. IEEE Trans. Ind. Appl. 52(2), 1265–1269 (2016)

    Google Scholar 

  27. H. Karimipour, V. Dinavahi, Extended Kalman filter based massively parallel dynamic state estimation. IEEE Trans. Smart Grid 6(3), 1539–1549 (2015)

    CrossRef  Google Scholar 

  28. Y. Zhong, X. Xu, L. Wang, IoT-enabled smart factory visibility and traceability using laser-scanners. Proc. Manuf. 10, 1–14 (2017). https://doi.org/10.1016/j.promfg.2017.07.103

    CrossRef  Google Scholar 

  29. Y. Zhang, T. Qu, O. Ho, G. Huang, Real-time work-in-progress management for smart object-enabled ubiquitous shop-floor environment. Int. J. Comput. Integr. Manuf. 24(5), 431–445 (2011). https://doi.org/10.1080/0951192X.2010.527374

    CrossRef  Google Scholar 

  30. A. Dehghantanha, A. Azmoodeh, K. Choo, Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans. Sustain. Comput. 4(1), 88–95 (2019a)

    CrossRef  Google Scholar 

  31. H. Said, T. Nicoletti, P. Perez, Utilizing telematics data to support effective equipment Fleet-management decisions: utilization rate and Hazard functions. J. Comput. Civ. Eng., 1–11 (2015). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000444

  32. Y. Xu, M. Chen, Improving just-in-time manufacturing operations by using internet of things based solutions. Procedia CIRP 56, 326–331 (2016). https://doi.org/10.1016/j.procir.2016.10.030

    CrossRef  Google Scholar 

  33. A. Dehghantanha, T. Dargahi, S. Grooby, A bibliometric analysis of authentication and access control in IoT devices, in Handbook of big data and IoT security, (Springer, 2019b), pp. 25–51. https://doi.org/10.1007/978-3-030-10543-3_3

  34. A. Dehghantanha, M. Conti, K.W. Franke, Internet of things security and forensics: Challenges and opportunities. Futur. Gener. Comput. Syst., 544–546 (2018a). https://doi.org/10.1016/j.future.2017.07.060

  35. M. Friendly, The Golden age of statistical graphics. Stat. Sci. 23(4), 502–535 (2008). https://doi.org/10.1214/08-STS268

    MathSciNet  CrossRef  MATH  Google Scholar 

  36. K. Vassakis, E. Petrakis, I. Kopanakis, Big Data Analytics: Applications, Prospects and Challenges, in Mobile Big Data, (Emmanuel Petrakis’s Lab, 2017). https://doi.org/10.1007/978-3-319-67925-9_1

  37. H. Karimipour, A. Dehghantanha, J. Sakhnini, in Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection. IEEE Int. Conf. on Smart Energy Grid Engineering (SEGE) (2019a), pp. 1–5

    Google Scholar 

  38. H. Karimipour, S. Mohammadi, V. Desai, Multivariate mutual information feature selection for intrusion detection. IEEE Canada Electr. Power Energy Conf. (EPEC), 1–6 (2018)

    Google Scholar 

  39. A. Vijayaraghavan, W. Sobel, A. Fox, D. Dornfeld, P. Warndorf, in Improving Machine Tool Interoperability Using Standardized Interface Protocols: MT Connect. International Symposium on Flexible Automation, (2008), pp. 1–6

    Google Scholar 

  40. GilPress. (2017, 10 1). What’s The Big Data? (Venturebeat) Retrieved 08 13, 2019, from The Chatbots Landscape: https://whatsthebigdata.com/2017/10/01/the-chatbots-landscape/

  41. P. Gölzer, P. Cato, M. Amberg, Data Processing Requirements of Industry 4.0 - Use Cases for Big Data Applications (Association for Information Systems (AISeL), 2015)

    Google Scholar 

  42. E. Hewitt, Cassandra: The Definitive Guide (O’Reilly Media, Inc., Sebastopol, 2011)

    Google Scholar 

  43. E. Anderson, X. Li, M. Shah, J. Tucek, J. Wylie, What Consistency Does Your Key-Value Store Actually Provide? (Hewlett-Packard Laboratories, 2009), pp. 1–6

    Google Scholar 

  44. K. Chodorow, S. Bradshaw, MongoDB: The Definitive Guide, in Powerful and Scalable Data Storage, 3rd edn., (O’Reilly Media, 2019), p. 425

    Google Scholar 

  45. H. Kagermann, J. Helbig, A. Hellinger, W. Wahlster, Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0, in Securing the Future of German Manufacturing Industry, (Forschungsunion, Acatech, 2013)

    Google Scholar 

  46. M. Santos, B. Martinho, C. Costa, Modelling and implementing big data warehouses for decision support. J. Manag. Anal. 4(2), 111–129 (2016). https://doi.org/10.1080/23270012.2017.1304292

    CrossRef  Google Scholar 

  47. L. Xu, N. Liang, Q. Gao, An integrated approach for agricultural ecosystem management - IEEE journals & magazine. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(4), 590–599 (2008). https://doi.org/10.1109/TSMCC.2007.913894

    CrossRef  Google Scholar 

  48. K. Shvachko, H. Kuang, S. Radia, R. Chansler, in The Hadoop Distributed File System. IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), 2010, pp. 1–10. doi:https://doi.org/10.1109/MSST.2010.5496972

  49. G. Jagannathan, R. Wright, in Research Track Poster Privacy-Preserving Distributed k-Means Clustering over Arbitrarily Partitioned Data *. Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (2005), pp. 593–599. https://doi.org/10.1145/1081870.1081942

  50. Y. Yao, Q. Cao, A. Vasilakos, EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Trans. Networking 23(3), 810–823 (2015). https://doi.org/10.1109/TNET.2014.2306592

    CrossRef  Google Scholar 

  51. A. Dehghantanha, O. Osanaiye, H. Cai, K.X. Choo, Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP J. Wirel. Commun. Netw., 1–20 (2016). https://doi.org/10.1186/s13638-016-0623-3

  52. F. Tao, L. Zhang, V. Venkatesh, Y. Luo, Y. Cheng, Cloud manufacturing: A computing and service-oriented manufacturing model. Proc. Inst. Mech. Eng. B J. Eng. 225(10), 1969–1976 (2011)

    CrossRef  Google Scholar 

  53. X. Xu, From cloud computing to cloud manufacturing. Robot. Comput. Integr. Manuf. 28(1), 75–86 (2012). https://doi.org/10.1016/j.rcim.2011.07.002

    CrossRef  Google Scholar 

  54. B. Daniel, Big data and analytics in higher education: Opportunities and challenges. Br. J. Educ. Technol. 46(5), 904–920 (2015). https://doi.org/10.1111/bjet.2015.46.issue-5

    CrossRef  Google Scholar 

  55. D. Delen, H. Demirkan, Data, information and analytics as services. Decis. Support. Syst. 55(1), 359–363 (2013). https://doi.org/10.1016/j.dss.2012.05.044

    CrossRef  Google Scholar 

  56. H.B. Karimipour, F. Derakhshan, in A Layered Intrusion Detection System for Critical Infrastructure Using Machine Learning. IEEE Int. Conf. on Smart Energy Grid Engineering (SEGE), (2019), pp. 1–5

    Google Scholar 

  57. W.P. Elderton, Tables for testing the goodness of fit of theory to observation. Biometrika 1(2), 155–163 (1902)

    Google Scholar 

  58. K. Pearson, Note on regression and inheritance in the case of two parents. Proc. R. Soc. Lond. 58, 240–242 (1895). https://doi.org/10.1098/rspl.1895.0041

    CrossRef  Google Scholar 

  59. A. Kramer, J. Green, J.T. Pollard, Causal analysis approaches in ingenuity pathway analysis | bioinformatics | Oxford Academic. Bioinformatics 30(4), 523–530 (2014). https://doi.org/10.1093/bioinformatics/btt703

    CrossRef  Google Scholar 

  60. J. Pearl, Simpson’s paradox, confounding, and collapibility (Cambridge University Press, Cambridge, 2000), pp. 173–200

    Google Scholar 

  61. S. Kleinberg, B. Mishra, The Temporal Logic of Causal Structures, in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, (AUAI Press, 2009), pp. 303–312

    Google Scholar 

  62. R. Agrawal, R. Srikant, in Fast Algorithms for Mining Association Rules. Proceedings of 20th International Conference Very Large Data Bases, 15(1215), 487–499 (1994)

    Google Scholar 

  63. J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation. ACM Sigmod Rec. 29(2), 1–12 (2000)

    CrossRef  Google Scholar 

  64. M. Zaki, Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000). https://doi.org/10.1109/69.846291

    CrossRef  Google Scholar 

  65. L. Duan, W. Street, Finding maximal fully-correlated itemsets in large databases. ICDM 9, 770–775 (2009)

    Google Scholar 

  66. E.R. Lapira, Fault Detection in a Network of Similar Machines Using Clustering Approach. Doctoral Dissertation, University of Cincinnati, 2012

    Google Scholar 

  67. H. Karimipour, A. Dehghantanha, R. Parizi, K. Choo, H. Leung, A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7 (2019b). https://doi.org/10.1109/ACCESS.2019.2920326

  68. A. Jalowiechki, P. Klusek, W. Skarka, The methods of knowledge acquisition in the product lifecycle for a generative model’s creation process. Proc. Manuf. 11, 2219–2226 (2017). https://doi.org/10.1016/j.promfg.2017.07.369

    CrossRef  Google Scholar 

  69. L. Alleman, L. Lamaison, P. Esperanza, PM10 metal concentrations and source identification using positive matrix factorization and wind sectoring in a French industrial zone. Atmos. Res. 96(4), 612–625 (2010). https://doi.org/10.1016/j.atmosres.2010.02.008

    CrossRef  Google Scholar 

  70. C.J. Kuo, D. Chen, L. Yang, H. Chen, Automatic machine status prediction in the era of industry 4.0: Case study of Machines in a Spring Factory. J. Syst. Archit. 81, 44–53 (2017). https://doi.org/10.1016/j.sysarc.2017.10.007

    CrossRef  Google Scholar 

  71. B. Bagheri, H. Ahmadi, R. Labbafi, Implementing discrete wavelet transform and artificial neural networks for acoustic condition monitoring of gearbox. Elixir Mech 35, 2909–2911 (2011)

    Google Scholar 

  72. J. Neter, M. Kutner, C. Nachtsheim, W. Wasserman, Applied Linear Statistical Models, 5th edn. (McGraw-Hill Irwin, New York, 1996), pp. 1–1415

    Google Scholar 

  73. D. Hosmer, S.S. Lemeshow, Applied Logistic Regression, 3rd edn. (Wiley, Hoboken, 2013)

    CrossRef  Google Scholar 

  74. P. Domingos, M. Pazzani, On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. 29(2), 103–130 (1997). https://doi.org/10.1023/A:1007413511361

    CrossRef  MATH  Google Scholar 

  75. N. Friedman, D. Geiger, M. Goldszmidt, Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997). https://doi.org/10.1023/A:1007465528199

    CrossRef  MATH  Google Scholar 

  76. M. Hagan, D. Howard, M. Beale, O. De Jesus, Neural Network Design, 2nd edn. (Martin Hagan, 2014)

    Google Scholar 

  77. A. Dehghantanha, H. Haddad Pajouh, R. Khayami, K. Choo, A deep recurrent neural network based approach for internet of things malware threat hunting. Futur. Gener. Comput. Syst. 85, 88–96 (2018b). https://doi.org/10.1016/j.future.2018.03.007

    CrossRef  Google Scholar 

  78. J. Suykens, J. Vandewalle, Least squares support vector machine classifiers. Neural. Process. Lett. 9(3), 293–300 (1999). https://doi.org/10.1023/A:1018628609742

    CrossRef  Google Scholar 

  79. B. Boser, I. Guyon, V. Vapnik, in A Training Algorithm for Optimal Margin Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, (1992), pp. 144–152

    Google Scholar 

  80. M. Maggio, H. Hoffmann, A. Papadopoulos, Comparison of decision-making strategies for self-optimization in autonomic computing systems. ACM Trans. Auton. Adapt. Syst. 7(4) (2012). https://doi.org/10.1145/2382570.2382572

  81. P. Bogdan, in A Cyber-Physical Systems Approach to Personalized Medicine: Challenges and Opportunities for NoC-Based Multicore Platforms. Design, Automation & Test in Europe Conference & Exhibition (DATE), (2015), pp. 2553–2258. https://doi.org/10.7873/DATE.2015.1127

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Al-Abassi, A., Karimipour, H., HaddadPajouh, H., Dehghantanha, A., Parizi, R.M. (2020). Industrial Big Data Analytics: Challenges and Opportunities. In: Choo, KK., Dehghantanha, A. (eds) Handbook of Big Data Privacy. Springer, Cham. https://doi.org/10.1007/978-3-030-38557-6_3

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