Advertisement

Big Data and Data Analytics in Aviation

  • Gerrit Burmester
  • Hui Ma
  • Dietrich Steinmetz
  • Sven HartmannnEmail author
Chapter

Abstract

Big Data technology in the field of aviation has emerged in recent years. Continuously growing amounts of data sources such as sensors, radars, cameras, weather stations, airports, etc. produce terabytes of high dynamic data each second. The future aviation concepts require modern data storing, data processing, and data analyzing technologies. The extraction of meaningful knowledge from the given data is a major challenge, trends, cross-connection, correlations, etc. have to be identified. Real-time critical tasks increase additionally the technology requirements and need innovative solutions. The application of Big Data technology in aviation context optimizes safety aspects, fuel consumption, maintenance processes, flight scheduling, etc. This chapter describes a process of Big Data application and summarizes relevant actual Big Data methods in the aviation domain.

References

  1. 1.
    The data science revolution that is transforming aviation (2018), https://www.forbes.com/sites/oliverwyman/2017/06/16/thedata-science-revolution-transforming-aviation/. Accessed 12 Feb 2018
  2. 2.
    A.Y. Zomaya, S. Sakr, Handbook of Big Data Technologies (Springer, Berlin, 2017)CrossRefGoogle Scholar
  3. 3.
    P. Russom et al., Big data analytics, in TDWI Best Practices Report, Fourth Quarter, vol. 19(4) (2011), pp. 1–34Google Scholar
  4. 4.
    H.-M. Chen, R. Schuetz, R. Kazman, F. Matthes, How Lufthansa capitalized on big data for business model renovation. MIS Q. Exec. 16(1) (2017)Google Scholar
  5. 5.
    L.R. Poole, A. Catalano, Real time visualization of sensor data in aircraft, in AUTOTESTCON 2004. Proceedings (IEEE, New York, 2004), pp. 389–394Google Scholar
  6. 6.
    A very short history of big data (2018), https://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-bigdata/. Accessed 12 Feb 2018
  7. 7.
  8. 8.
    T. Davenport, Big Data at Work: Dispelling the Myths, Uncovering the Opportunities (Harvard Business Review Press, 2014)Google Scholar
  9. 9.
    R. Bryant, R.H. Katz, E.D. Lazowska, Big-Data Computing: Creating Revolutionary Breakthroughs in Commerce, Science and Society (2008)Google Scholar
  10. 10.
    S. Yin, O. Kaynak, Big data for modern industry: challenges and trends [point of view]. Proc. IEEE 103(2), 143–146 (2015)CrossRefGoogle Scholar
  11. 11.
    Die 9 V von Big Data (2018), https://digitales-wirtschaftswunder.de/die-9-v-von-big-data/. Accessed 12 Feb 2018
  12. 12.
    Four Vs Big Data (2018), https://www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed 12 Feb 2018
  13. 13.
    Updated for 2017: The V’s of big data: velocity, volume, value, variety, and veracity (2017), https://www.xsnet.com/blog/updated-for-2017- the-vs-of-big-data-velocity-volume-value-varietyand-veracity. Accessed 12 Feb 2018
  14. 14.
    Big data in planes: new P and W GTF engine telemetry to generate 10GB/s (2018), https://www.vrworld.com/2015/05/08/big-data-in-planes-newpw-gtf-engine-telemetry-to-generate-10gbs/. Accessed 12 Feb 2018
  15. 15.
    Uber Elevate (2018), https://www.uber.com/info/elevate/. Accessed 12 Feb 2018
  16. 16.
    D. Steinmetz, G. Burmester, S. Hartmann, A fast heuristic for finding near-optimal groups for vehicle platooning in road networks, in Proceedings of the International Conference on Database and Expert Systems Applications (Springer, 2017), pp. 395–405Google Scholar
  17. 17.
    M. Simons, Model aircraft aerodynamics (Chris Lloyd Sales & Marketing, 2000)Google Scholar
  18. 18.
    S. Li, Y. Yang, L. Yang, H. Su, G. Zhang, J. Wang, Civil aircraft big data platform, in IEEE 11th International Conference on Semantic Computing (ICSC), 2017 (IEEE, New York, 2017), pp. 328–333Google Scholar
  19. 19.
    W. Miao, D. Zheng, G. Hangyu, Y. Tao, Research on big data management and analysis method of multi-platform avionics system, in IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) (2017), pp. 757–761.  https://doi.org/10.1109/ICIS.2017.7960094
  20. 20.
    D. Kulkarni, Y. Wang, M. Windrem, H. Patel, R. Keller, Aviation Data Integration System (2003)Google Scholar
  21. 21.
    S. Aulbach, T. Grust, D. Jacobs, A. Kemper, J. Rittinger, Multi-tenant databases for software as a service: schema-mapping techniques, in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (ACM, 2008), pp. 1195–1206Google Scholar
  22. 22.
    C. Batini, M. Lenzerini, S.B. Navathe, A comparative analysis of methodologies for database schema integration. ACM Comput. Surv. (CSUR) 18(4), 323–364 (1986)CrossRefGoogle Scholar
  23. 23.
    C. Esposito, M. Ficco, F. Palmieri, A. Castiglione, A knowledge-based platform for big data analytics based on publish/subscribe services and stream processing. Knowl. Based Syst. 79, 3–17 (2015)CrossRefGoogle Scholar
  24. 24.
    X.L. Dong, D. Srivastava, Big data integration, in 2013 IEEE 29th International Conference on Data Engineering (ICDE) (IEEE, New York, 2013), pp. 1245–1248Google Scholar
  25. 25.
    A. Gruenheid, X.L. Dong, D. Srivastava, Incremental record linkage. Proc. VLDB Endow. 7(9), 697–708 (2014)CrossRefGoogle Scholar
  26. 26.
    A. Moniruzzaman, S.A. Hossain, Nosql database: new era of databases for big data analytics-classification, characteristics and comparison, in arXiv preprint (2013), arXiv:1307.0191
  27. 27.
    A. Dhar, U. Student, Big data technologies for batch and real-time data processing: A. Int. J. Eng. Sci. 15232 (2017)Google Scholar
  28. 28.
    J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  29. 29.
    S. Salloum, R. Dautov, X. Chen, P.X. Peng, J.Z. Huang, Big data analytics on apache spark. Int. J. Data Sci. Anal. 1(3), 145–164 (2016).  https://doi.org/10.1007/s41060-016-0027-9. ISSN: 2364-4168CrossRefGoogle Scholar
  30. 30.
    N. H. Motlagh, T. Taleb, O. Arouk, Low-altitude unmanned aerial vehicles-based internet of things services: comprehensive survey and future perspectives. IEEE Intern. Things J. 3(6), 899–922 (2016).  https://doi.org/10.1109/JIOT.2016.2612119. ISSN: 2327-4662
  31. 31.
    S. Sarkar, X. Jin, A. Ray, Data-driven fault detection in aircraft engines with noisy sensor measurements. J. Eng. Gas Turbin. Power 133(8), 081602 (2011)CrossRefGoogle Scholar
  32. 32.
    E.C. Larson, B.E. Parker, B.R. Clark, Model-based sensor and actuator fault detection and isolation, in Proceedings of the 2002. American Control Conference, 2002, vol. 5 (IEEE, New York, 2002), pp. 4215–4219Google Scholar
  33. 33.
    S. García, J. Luengo, F. Herrera, Data Preprocessing in Data Mining (Springer, Berlin, 2015)CrossRefGoogle Scholar
  34. 34.
    F. Chen, P. Deng, J. Wan, D. Zhang, A.V. Vasilakos, X. Rong, Data mining for the internet of things: literature review and challenges. Int. J. Distrib. Sens. Netw. 11(8), 431047 (2015)CrossRefGoogle Scholar
  35. 35.
    A. A. Christopher, S. A. alias Balamurugan, Prediction of warning level in aircraft accidents using data mining techniques. Aeronautical J. 118(1206), 935–952 (2014), pp. 935–952CrossRefGoogle Scholar
  36. 36.
    V.A. Skormin, V.I. Gorodetski, L.J. Popyack, Data mining technology for failure prognostic of avionics. IEEE Trans. Aerosp. Electron. Syst. 38(2), 388–403 (2002)CrossRefGoogle Scholar
  37. 37.
    I.X. Castilho, Fault prediction in aircraft tires using Bayesian Networks (2015)Google Scholar
  38. 38.
    S. Imai, A. Galli, C.A. Varela, Dynamic data-driven avionics systems: inferring failure modes from data streams. Proc. Comput. Sci. 51(Supplement C) (2015), pp. 1665–1674.  https://doi.org/10.1016/j.procs.2015.05.301. ISSN: 1877-0509CrossRefGoogle Scholar
  39. 39.
    R. Klockowski, S. Imai, C.L. Rice, C.A. Varela, Autonomous data error detection and recovery in streaming applications. Proc. Comput. Sci. 18, 2036–2045 (2013)CrossRefGoogle Scholar
  40. 40.
    K.-C. Wong, A short survey on data clustering algorithms, in 2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI) (IEEE, New York, 2015), pp. 64–68Google Scholar
  41. 41.
    J.-G. Lee, J. Han, K.-Y. Whang, Trajectory clustering: a partition-andgroup framework, in Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data (ACM, New Jersey, 2007), pp. 593–604Google Scholar
  42. 42.
    S. Ayhan, H. Samet, Diclerge: Divide-cluster-merge framework for clustering aircraft trajectories, in Proceedings of the 8th ACM SIGSPATIAL IWCTS (Seattle, WA, 2015)Google Scholar
  43. 43.
    Predictive Maintenance System - PowerBI (2018), https://powerbi.microsoft.com/en-us/industries/airline/. Accessed 12 Feb 2018

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Gerrit Burmester
    • 1
  • Hui Ma
    • 2
  • Dietrich Steinmetz
    • 1
  • Sven Hartmannn
    • 1
    Email author
  1. 1.Clausthal University of TechnologyClausthal-ZellerfeldGermany
  2. 2.Victoria University of WellingtonWellingtonNew Zealand

Personalised recommendations