Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Compression of Mobile Location Data

  • Goce TrajcevskiEmail author
  • Ouri Wolfson
  • Peter Scheuermann
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_73


Location sensing and compression; Spatiotemporal data reduction


Miniaturization of computing, sending, and networking devices has provided the technological foundation for applications which generate huge volumes of location-in-time data – order of petabytes (PB) annually from smart phones alone [12]. In moving objects databases (MOD) [9], the data pertaining to the whereabouts of a given mobile object is commonly represented as a sequence of (location, time) points, ordered by the temporal dimension. Depending on the application’s settings, such points may be obtained by different means, e.g., an onboard GPS-based system, RFID sensors, roadside sensors [18], base stations in a cellular architecture, etc. The main motivation for compressing the location data of a given (collection of) moving object(s) is twofold: (1) Reducing the storage requirements, in addition to smart phones [12], location samples from onboard GPS devices taken once every 5 s, can still...

This is a preview of subscription content, log in to check access.


  1. 1.
    Alt H, Guibas L. Discrete geometric shapes: matching, interpolation, and approximation. In: Handbook of computational geometry. Elsevier Science Publishers; 1999.zbMATHGoogle Scholar
  2. 2.
    Alt A, Knauer C, Wenk C. Comparison of distance measures for planar curves. Algorithmica. 2004;38.MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Barequet G, Chen DZ, Deascu O, Goodrich MT, Snoeyink J. Efficiently approximating polygonal path in three and higher dimensions. Algorithmica. 2002;33(2):150–167.MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Cao H, Wolfson O, Trajcevski G. Spatio-temporal data reduction with deterministic error bounds. VLDB J. 2006;15(3):211–28.CrossRefGoogle Scholar
  5. 5.
    Chan W, Chin F. Approximation of polygonal curves with minimum number of line segments or minimal error. Int J Comput Geom Appl. 1996;6(1): 59–77.zbMATHCrossRefGoogle Scholar
  6. 6.
    Douglas D, Peucker T. Algorithms for the reduction of the number of points required to represent a digitised line or its caricature. Can Cartogr. 1973;10(2):112–22.CrossRefGoogle Scholar
  7. 7.
    Faraway JJ, Reed MP, Wang J. Modelling three-dimensional trajectories by using Bézier curves with application to hand motion. Appl Stat. 2007;56(5):571–85.Google Scholar
  8. 8.
    Ghica O, Trajcevski G, Wolfson O, Buy U, Scheuermann P, Zhou F, Vaccaro D. Trajectory data reduction in wireless sensor networks. IJNGC. 2010;1(1): 28–51.Google Scholar
  9. 9.
    Güting RH, Schneider M. Moving objects databases. San Francisco: Morgan Kaufmann; 2005.zbMATHGoogle Scholar
  10. 10.
    Hershberger J, Snoeyink J. Speeding up the Douglas-Peuker line-simplification algorithm. In: Proceedings of the 5th International Symposium on Spatial Data Handling; 1992.Google Scholar
  11. 11.
    Jensen CS, Lin D, Ooi BC. Continuous clustering of moving objects. IEEE Trans Knowl Data Eng. 2007;19(9):1161–1174.CrossRefGoogle Scholar
  12. 12.
    Mc Kansey Global Institute. Big data: the next frontier for innovation, competition, and productivity; 2011.Google Scholar
  13. 13.
    Popa IS, Zeitouni K, Oria V, Kharrat A. Spatio-temporal compression of trajectories in road networks. GeoInformatica. 2014. https://doi.org.10.1007/s10707-014-0208-4.Google Scholar
  14. 14.
    Sayood K. Introduction to data compression. San Francisco: Morgan Kaufmann; 1996.zbMATHGoogle Scholar
  15. 15.
    Schiller J, Voisard A. Location-based services. San Francisco: Morgan Kaufmann; 2004.Google Scholar
  16. 16.
    Trajcevski G, Wolfson O, Hinrichs K, Chamberlain S. Managing uncertainty in moving objects databases. ACM Trans Database Syst. 2004;29(3):463–507.CrossRefGoogle Scholar
  17. 17.
    Trajcevski G, Cao H, Wolfson O, Scheuermann P, Vaccaro D. On-line data reduction and the quality of history in moving objects databases. In: Proceedings of the 5th ACM International Workshop on Data Engineering for Wireless and Mobile Access; 2006.Google Scholar
  18. 18.
    Turner-Fairbank Highway Research Center. Traffic detector handbook, vol. I. 3rd ed. McLean: U.S. Department of transportation; 2006.Google Scholar
  19. 19.
    Vlachos M, Hadjielefteriou M, Gunopulos D, Keogh E. Indexing multidimensional time-series. VLDB J. 2006;15(1):1–20.CrossRefGoogle Scholar
  20. 20.
    Weibel R. Generalization of spatial data: principles and selected algorithms. In: Algorithmic foundations of geographic information systems. LNCS. Springer; 1998.Google Scholar
  21. 21.
    Wolfson O, Sistla AP, Chamberlain S, Yesha Y. Updating and querying databases that track mobile units. Distrib Parallel Databases. 1999;7(3):257–88.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Goce Trajcevski
    • 1
    Email author
  • Ouri Wolfson
    • 2
    • 3
  • Peter Scheuermann
    • 1
  1. 1.Department of ECpEIowa State UniversityAmesUSA
  2. 2.Mobile Information Systems Center (MOBIS)The University of Illinois at ChicagoChicagoUSA
  3. 3.Department of CSUniversity of Illinois at ChicagoChicagoUSA