Scalable Generation of Synthetic GPS Traces with Real-Life Data Characteristics

  • Konrad Bösche
  • Thibault Sellam
  • Holger Pirk
  • René Beier
  • Peter Mieth
  • Stefan Manegold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7755)

Abstract

Database benchmarking is most valuable if real-life data and workloads are available. However, real-life data (and workloads) are often not publicly available due to IPR constraints or privacy concerns. And even if available, they are often limited regarding scalability and variability of data characteristics. On the other hand, while easily scalable, synthetically generated data often fail to adequately reflect real-life data characteristics. While there are well established synthetic benchmarks and data generators for, e.g., business data (TPC-C, TPC-H), there is no such up-to-date data generator, let alone benchmark, for spatiotemporal and/or moving objects data.

In this work, we present a data generator for spatiotemporal data. More specifically, our data generator produces synthetic GPS traces, mimicking the GPS traces that GPS navigation devices generate. To this end, our generator is fed with real-life statistical profiles derived from the user base and uses real-world road network information. Spatial scalability is achieved by choosing statistics from different regions. The data volume can be scaled by tuning the number and length of the generated trajectories. We compare the generated data to real-life data to demonstrate how well the synthetically generated data reflects real-life data characteristics.

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References

  1. 1.
    Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)MATHCrossRefGoogle Scholar
  2. 2.
    Delling, D., Sanders, P., Schultes, D., Wagner, D.: Engineering route planning algorithms. In: Algorithmics of Large and Complex Networks, pp. 117–139 (2009)Google Scholar
  3. 3.
    Düntgen, C., Behr, T., Güting, R.: Berlinmod: a benchmark for moving object databases. The VLDB Journal 18, 1335–1368 (2009), doi:10.1007/s00778-009-0142-5CrossRefGoogle Scholar
  4. 4.
    Haklay, M., Weber, P.: Openstreetmap: User-generated street maps. IEEE Pervasive Computing 7(4), 12–18 (2008)CrossRefGoogle Scholar
  5. 5.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths, pp. 100–107 (1968)Google Scholar
  6. 6.
    Hilger, M., Köhler, E., Möhring, R., Schilling, H.: Fast point-to-point shortest path computations with arc-flags. The Shortest Path Problem: Ninth DIMACS Implementation Challenge 74, 41–72 (2009)Google Scholar
  7. 7.
    Pfoser, D., Theodoridis, Y.: Generating semantics-based trajectories of moving objects. Computers, Environment and Urban Systems 27(3), 243–263 (2003)CrossRefGoogle Scholar
  8. 8.
    Liu, D.V.V.R., Watling, D.P.: Dracula: Dynamic route assignment combining user learning and microsimulation. In: PTRC, E (1994)Google Scholar
  9. 9.
    Rickert, M., Wagner, P., Gawron, C.: Real-time simulation of the german autobahn network (1997)Google Scholar
  10. 10.
    Saglio, J.-M., Moreira, J.: Oporto: A realistic scenario generator for moving objects. In: DEXA Workshop, pp. 426–432 (1999)Google Scholar
  11. 11.
    Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the Generation of Spatiotemporal Datasets. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651, pp. 147–164. Springer, Heidelberg (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Konrad Bösche
    • 1
  • Thibault Sellam
    • 2
  • Holger Pirk
    • 2
  • René Beier
    • 1
  • Peter Mieth
    • 1
  • Stefan Manegold
    • 2
  1. 1.TomTomBerlinGermany
  2. 2.Centrum Wiskunde & Informatica (CWI)AmsterdamThe Netherlands

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