Abstract
Connected vehicles are capable of collecting, through their embedded sensors, and transmitting huge amounts of data at very high frequencies. Leveraging this data can be valuable for many entities: automobile manufacturer, vehicles owners, third parties, etc. Indeed, this “big data” can be used in a large broad of services ranging from road safety services to aftermarket services (e.g., predictive and preventive maintenance). Nevertheless, processing and storing big data raised new scientific and technological challenges that traditional approaches cannot handle efficiently. In this paper, we address the issue of online (i.e., near real-time) data processing of automotive information. More precisely, we focus on the performance of data fusion to support several millions of connected vehicles. In order to face this performance challenge, we propose novel approaches, based on spatial indexation, to speed up our automotive application. To validate the effectiveness of our proposal, we have implemented and conducted real experiments on PSA Group (PSA Group is the second-largest automobile manufacturer in Europe with about 3 million sold vehicles in 2015) big data streaming platform. The experimental results have demonstrated the efficiency of our spatial indexing and querying techniques.
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The ISO 11898 standard specifies that the CAN physical layer allows transmission rates up to 1 Mbit/s for use within road vehicles. See http://www.iso.org/iso/catalogue_detail.htm?csnumber=33423.
PSA Group is planning for 2020 to handle data from nearly 5 millions cars around the country (e.g., France).
The terms processing and computing are interchangeable in the rest of the paper.
A processing element is a thread executing executes a set of operators instances.
References
Apache: Hadoop. https://hadoop.apache.org/. Version 2.6.3
Berchtold S, Keim DA, Kriegel HP (1996) The x-tree: an index structure for high-dimensional data. In: Proceedings of the 22th international conference on very large data bases, VLDB ’96. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 28–39
Brinkhoff T, Kriegel HP, Schneider R (1993) Comparison of approximations of complex objects used for approximation-based query processing in spatial database systems. In: Data engineering, 1993. Proceedings. Ninth International Conference on, pp 40–49
Finkel RA, Bentley JL (1974) Quad trees a data structure for retrieval on composite keys. Acta Inform 4(1):1–9
Fournier A, Montuno DY (1984) Triangulating simple polygons and equivalent problems. ACM Trans Graph 3(2):153–174
GOOGLE: Keyhole markup language. https://developers.google.com/kml/
Gordon MI, Thies W, Amarasinghe S (2006) Exploiting coarse-grained task, data, and pipeline parallelism in stream programs. SIGARCH Comput Archit News 34(5):151–162
Guttman A (1984) R-trees: a dynamic index structure for spatial searching. SIGMOD Rec 14(2):47–57
Haines E (1994) Graphics gems iv. chap. Point in polygon strategies. Academic Press Professional, Inc, San Diego
Hill MD (1992) Scalable shared memory multiprocessors, chap. What is scalability?. Springer, Boston
Hirzel M, Soulé R, Schneider S, Gedik B, Grimm R (2014) A catalog of stream processing optimizations. ACM Comput Surv 46(4):46:1–46:34
IBM: Ibm streams: capture and analyze data in motion. http://www-03.ibm.com/software/products/en/ibm-streams
IBM: iscontained. http://www-01.ibm.com/support/knowledgecenter/SSCRJU_3.2.1/
Isaacson C (2014) Understanding big data scalability: big data scalability series, 1st edn. Prentice Hall, Upper Saddle River
Kirkpatrick DG (1983) Optimal search in planar subdivisions. SIAM J Comput 12(1):28–35
Labrinidis A, Jagadish H (2012) Challenges and opportunities with big data. Proc VLDB Endow 5(12):2032–2033
Lee E, Lee EK, Gerla M, Oh SY (2014) Vehicular cloud networking: architecture and design principles. IEEE Commun Mag 52(2):148–155. doi:10.1109/MCOM.2014.6736756
Lipton RJ, Dobkin DP (1976) Complexity measures and hierarchies for the evaluation of integers and polynomials. Theor Comput Sci 3(3):349–357
MacMartin S et al (1992) Fastest point in polygon test. Ray Tracing News 5(3)
Météo france. http://www.meteofrance.com
Niemeyer G Geohash. http://geohash.org/
Openstreetmap. http://export.openstreetmap.fr/contours-administratifs/
Orenstein JA (1989) Redundancy in spatial databases. SIGMOD Rec 18(2):295–305
O’Rourke J (1985) Finding minimal enclosing boxes. Int J Comput Inf Sci 14(3):183–199
Preparata FP, Hong SJ (1977) Convex hulls of finite sets of points in two and three dimensions. Commun ACM 20(2):87–93
Preparata FP, Shamos MI (1985) Computational geometry—an introduction. Texts and monographs in computer science. Springer, Heidelberg
Rosenfeld A (1975) A converse to the Jordan curve theorem for digital curves. Inf Control 29(3):292–293
Sahr K, White D, Kimerling AJ (2003) Geodesic discrete global grid systems. Cartogr Geogr Inf Sci 30(2):121–134
Samet H, Rosenfeld A, Shaffer CA, Webber RE (1984) A geographic information system using quadtrees. Pattern Recognit 17(6):647–656
Seidel R (1991) A simple and fast incremental randomized algorithm for computing trapezoidal decompositions and for triangulating polygons. Comput Geom 1:51–64
Shahrivari S (2014) Beyond batch processing: towards real-time and streaming big data. CoRR. arXiv:abs/1403.3375
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Haroun, A., Mostefaoui, A. & Dessables, F. Data fusion in automotive applications. Pers Ubiquit Comput 21, 443–455 (2017). https://doi.org/10.1007/s00779-017-1008-2
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DOI: https://doi.org/10.1007/s00779-017-1008-2