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.
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References
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
A.Y. Zomaya, S. Sakr, Handbook of Big Data Technologies (Springer, Berlin, 2017)
P. Russom et al., Big data analytics, in TDWI Best Practices Report, Fourth Quarter, vol. 19(4) (2011), pp. 1–34
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)
L.R. Poole, A. Catalano, Real time visualization of sensor data in aircraft, in AUTOTESTCON 2004. Proceedings (IEEE, New York, 2004), pp. 389–394
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
Big data the next frontier for innovation (2018), https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation. Accessed 12 Feb 2018
T. Davenport, Big Data at Work: Dispelling the Myths, Uncovering the Opportunities (Harvard Business Review Press, 2014)
R. Bryant, R.H. Katz, E.D. Lazowska, Big-Data Computing: Creating Revolutionary Breakthroughs in Commerce, Science and Society (2008)
S. Yin, O. Kaynak, Big data for modern industry: challenges and trends [point of view]. Proc. IEEE 103(2), 143–146 (2015)
Die 9 V von Big Data (2018), https://digitales-wirtschaftswunder.de/die-9-v-von-big-data/. Accessed 12 Feb 2018
Four Vs Big Data (2018), https://www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed 12 Feb 2018
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
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
Uber Elevate (2018), https://www.uber.com/info/elevate/. Accessed 12 Feb 2018
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. 395–405
M. Simons, Model aircraft aerodynamics (Chris Lloyd Sales & Marketing, 2000)
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. 328–333
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
D. Kulkarni, Y. Wang, M. Windrem, H. Patel, R. Keller, Aviation Data Integration System (2003)
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. 1195–1206
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)
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)
X.L. Dong, D. Srivastava, Big data integration, in 2013 IEEE 29th International Conference on Data Engineering (ICDE) (IEEE, New York, 2013), pp. 1245–1248
A. Gruenheid, X.L. Dong, D. Srivastava, Incremental record linkage. Proc. VLDB Endow. 7(9), 697–708 (2014)
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
A. Dhar, U. Student, Big data technologies for batch and real-time data processing: A. Int. J. Eng. Sci. 15232 (2017)
J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
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: 2364-4168
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
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)
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. 4215–4219
S. GarcÃa, J. Luengo, F. Herrera, Data Preprocessing in Data Mining (Springer, Berlin, 2015)
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)
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. 935–952
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)
I.X. Castilho, Fault prediction in aircraft tires using Bayesian Networks (2015)
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: 1877-0509
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)
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. 64–68
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. 593–604
S. Ayhan, H. Samet, Diclerge: Divide-cluster-merge framework for clustering aircraft trajectories, in Proceedings of the 8th ACM SIGSPATIAL IWCTS (Seattle, WA, 2015)
Predictive Maintenance System - PowerBI (2018), https://powerbi.microsoft.com/en-us/industries/airline/. Accessed 12 Feb 2018
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Burmester, G., Ma, H., Steinmetz, D., Hartmannn, S. (2018). Big Data and Data Analytics in Aviation. In: Durak, U., Becker, J., Hartmann, S., Voros, N. (eds) Advances in Aeronautical Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-75058-3_5
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DOI: https://doi.org/10.1007/978-3-319-75058-3_5
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