Skip to main content

3DGraCT: A Grammar-Based Compressed Representation of 3D Trajectories

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11147))

Abstract

Much research has been published about trajectory management on the ground or at the sea, but compression or indexing of flight trajectories have usually been less explored. However, air traffic management is a challenge because airspace is becoming more and more congested, and large flight data collections must be preserved and exploited for varied purposes. This paper proposes 3DGraCT, a new method for representing these flight trajectories. It extends the GraCT compact data structure to cope with a third dimension (altitude), while retaining its space/time complexities. 3DGraCT improves space requirements of traditional spatio-temporal data structures by two orders of magnitude, being competitive for the considered types of queries, even leading the comparison for a particular one.

This work was funded in part by EU H2020 MSCA RISE BIRDS: 690941; MINECO-AEI/FEDER-UE: TIN2016-78011-C4-1-R; MINECO-CDTI/FEDER-UE CIEN IDI-20141259; MINECO-CDTI/FEDER-UE CIEN IDI-20150616; MINECO-CDTI/FEDER-UE INNTERCONECTA ITC-20161074; Xunta de Galicia/FEDER-UE ED431C 2017/58 and ED431G/01.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    From now on, we will refer to them simply as objects or moving objects.

  2. 2.

    Note that only shaded structures are used to encode the snapshot, the other ones are used for illustration purposes.

  3. 3.

    \(\langle z,x,y \rangle \) notation indicated that these three values are packed in a 32-bit integer.

  4. 4.

    https://opensky-network.org/.

References

  1. de Bernardo, G., Álvarez-García, S., Brisaboa, N.R., Navarro, G., Pedreira, O.: Compact querieable representations of raster data. In: Kurland, O., Lewenstein, M., Porat, E. (eds.) SPIRE 2013. LNCS, vol. 8214, pp. 96–108. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02432-5_14

    Chapter  Google Scholar 

  2. Botea, V., Mallett, D., Nascimento, M.A., Sander, J.: PIST: an efficient and practical indexing technique for historical spatio-temporal point data. GeoInformatica 12(2), 143–168 (2008)

    Article  Google Scholar 

  3. Brisaboa, N., Ladra, S., Navarro, G.: DACs: bringing direct access to variable-length codes. Inf. Process. Manag. 49(1), 392–404 (2013)

    Article  Google Scholar 

  4. Brisaboa, N.R., Gómez-Brandón, A., Navarro, G., Paramá, J.R.: GraCT: a grammar based compressed representation of trajectories. In: Inenaga, S., Sadakane, K., Sakai, T. (eds.) SPIRE 2016. LNCS, vol. 9954, pp. 218–230. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46049-9_21

    Chapter  Google Scholar 

  5. Brisaboa, N.R., Ladra, S., Navarro, G.: Compact representation of web graphs with extended functionality. Inf. Syst. 39(1), 152–174 (2014)

    Article  Google Scholar 

  6. Cudre-Mauroux, P., Wu, E., Madden, S.: Trajstore: an adaptive storage system for very large trajectory data sets. In: Proceedings of the IEEE 26th International Conference on Data Engineering (ICDE 2010), pp. 109–120 (2010)

    Google Scholar 

  7. Deng, K., Xie, K., Zheng, K., Zhou, X.: Trajectory indexing and retrieval. In: Zheng, Y., Zhou, X. (eds.) Computing with Spatial Trajectories, pp. 35–60. Springer, New York (2011). https://doi.org/10.1007/978-1-4614-1629-6_2

    Chapter  Google Scholar 

  8. Douglas, D.H., Peuker, T.K.: Algorithms for the reduction of the number of points required to represent a line or its caricature. Can. Cartogr. 10(2), 112–122 (1973)

    Article  Google Scholar 

  9. Gog, S., Beller, T., Moffat, A., Petri, M.: From theory to practice: plug and play with succinct data structures. In: Gudmundsson, J., Katajainen, J. (eds.) SEA 2014. LNCS, vol. 8504, pp. 326–337. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07959-2_28

    Chapter  Google Scholar 

  10. Jacobson, G.: Space-efficient static trees and graphs. In: IEEE Symposium on Foundations of Computer Science (FOCS), pp. 549–554 (1989)

    Google Scholar 

  11. Knuth, D.E.: Efficient representation of perm groups. Combinatorica 11, 33–43 (1991)

    Article  MathSciNet  Google Scholar 

  12. Larsson, N.J., Moffat, A.: Off-line dictionary-based compression. Proc. IEEE 88(11), 1722–1732 (2000)

    Article  Google Scholar 

  13. Nascimento, M.A., Silva, J.R.O.: Towards historical R-trees. In: Proceedings of the 1998 ACM Symposium on Applied Computing. SAC 1998, pp. 235–240. ACM (1998)

    Google Scholar 

  14. Navarro, G.: Compact Data Structures - A Practical Approach. Cambridge University Press, Cambridge (2016)

    Book  Google Scholar 

  15. Nibali, A., He, Z.: Trajic: an effective compression system for trajectory data. IEEE Trans. Knowl. Data Eng. 27(11), 3138–3151 (2015)

    Article  Google Scholar 

  16. Schäfer, M., Strohmeier, M., Lenders, V., Martinovic, I., Wilhelm, M.: Bringing up OpenSky: a large-scale ADS-B sensor network for research. In: Proceedings of the 13th International Symposium on Information Processing in Sensor Networks. IPSN 2014, pp. 83–94. IEEE Press, Piscataway (2014). http://dl.acm.org/citation.cfm?id=2602339.2602350

  17. Tao, Y., Papadias, D.: MV3R-tree: a spatio-temporal access method for timestamp and interval queries. In: 2001 Proceedings of the 27th International Conference on Very Large Data Bases, VLDB, pp. 431–440 (2001)

    Google Scholar 

  18. Trajcevski, G., Cao, H., Scheuermann, P., Wolfson, O., Vaccaro, D.: On-line data reduction and the quality of history in moving objects databases. In: Proceedings of the Fifth ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 19–26 (2006)

    Google Scholar 

  19. Vazirgiannis, M., Theodoridis, Y., Sellis, T.K.: Spatio-temporal composition and indexing for large multimedia applications. ACM Multimed. Syst. J. 6(4), 284–298 (1998)

    Article  Google Scholar 

  20. Wandelt, S., Sun, X.: Efficient compression of 4D-trajectory data in air traffic management. IEEE Trans. Intell. Transp. Syst. 16(2), 844–853 (2015)

    Google Scholar 

  21. Wandelt, S., Sun, X., Fricke, H.: ADS-BI: compressed indexing of ADS-B data. IEEE Trans. Intell. Transp. Syst. 99, 1–12 (2018)

    Article  Google Scholar 

  22. Wandelt, S., Sun, X., Gollnick, V.: SO6C: compressed trajectories in air traffic management. Air Traffic Control Q. 22(2), 157–178 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adrián Gómez-Brandón .

Editor information

Editors and Affiliations

Appendix

Appendix

The datasets used in our experimentaion have been obtained from the OpenSky NetworkFootnote 4. We have chosen ADS-B messages broadcasted by aircrafts of 30 different airlines and describe flights between 30 European airports:

  • Airlines (ICAO code): AEA, AEE, AFR, AUA, AZA, BAW, BEE, BEL, BER, DLH, EIN, EWG, EZS, EZY, FDX, FIN, GWI, IBE, IBK, IBS, KLM, LOT, NAX, NLY, RYR, SAS, SHT, SWR, TAP, and VLG.

  • Airports (ICAO code): EBBR, EDDF, EDDK, EDDL, EDDM, EDDT, EFHK, EGCC, EGKK, EGLL, EGPH, EGSS, EHAM, EIDW, EKCH, ENGM, EPWA, ESSA, LEBL, LEMD, LEPA, LFPG, LFPO, LGAV, LIMC, LIRF, LOWW, LPPT, LSGG, and LSZH.

ADS-B messages were captured from 2017-01-02 to 2017-01-31, and sampled as follows:

  • 1day : 2017-01-02.

  • 1week: 2017-01-02 – 2017-01-08.

  • 2weeks: 2017-01-02 – 2017-01-15.

  • 1month: 2017-01-02 – 2017-01-31.

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Brisaboa, N.R., Gómez-Brandón, A., Martínez-Prieto, M.A., Paramá, J.R. (2018). 3DGraCT: A Grammar-Based Compressed Representation of 3D Trajectories. In: Gagie, T., Moffat, A., Navarro, G., Cuadros-Vargas, E. (eds) String Processing and Information Retrieval. SPIRE 2018. Lecture Notes in Computer Science(), vol 11147. Springer, Cham. https://doi.org/10.1007/978-3-030-00479-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00479-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00478-1

  • Online ISBN: 978-3-030-00479-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics