Hermoupolis: A Trajectory Generator for Simulating Generalized Mobility Patterns

  • Nikos Pelekis
  • Christos Ntrigkogias
  • Panagiotis Tampakis
  • Stylianos Sideridis
  • Yannis Theodoridis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8190)


During the last decade, the domain of mobility data mining has emerged providing many effective methods for the discovery of intuitive patterns representing collective behavior of trajectories of moving objects. Although a few real-world trajectory datasets have been made available recently, these are not sufficient for experimentally evaluating the various proposals, therefore, researchers look to synthetic trajectory generators. This case is problematic because, on the one hand, real datasets are usually small, which compromises scalability experiments, and, on the other hand, synthetic dataset generators have not been designed to produce mobility pattern driven trajectories. Motivated by this observation, we present Hermoupolis, an effective generator of synthetic trajectories of moving objects that has the main objective that the resulting datasets support various types of mobility patterns (clusters, flocks, convoys, etc.), as such producing datasets with available ground truth information.


Mobility Data Mining Trajectory Patterns Synthetic Generators 


  1. 1.
    Brinkhoff, T.: Generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    Duntgen, C., Behr, T., Guting, R.H.: BerlinMOD: a benchmark for moving object databases. The VLDB Journal 18(6), 34 (2008)Google Scholar
  3. 3.
    Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Trajectory Pattern Mining. In: Proc. of SIGKDD (2007)Google Scholar
  4. 4.
    Giannotti, F., Mazzoni, A., Puntoni, S., Renso, C.: Synthetic generation of cellular network positioning data. In: Proc. of ACM GIS, pp. 12–20 (2005)Google Scholar
  5. 5.
    Gudmundsson, J., Kreveld, M.J., Speckmann, B.: Efficient detection of patterns in 2d trajectories of moving points. GeoInformatica 11(2), 195–215 (2007)CrossRefGoogle Scholar
  6. 6.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of Convoys in Trajectory Databases. In: Proc. of VLDB (2008)Google Scholar
  7. 7.
    Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 364–381. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Laube, P., Imfeld, S., Weibel, R.: Discovering relative motion patterns in groups of moving point objects. IJGIS 19(6), 639–668 (2005)Google Scholar
  9. 9.
    Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: A partition-and-group framework. In: Proc. of SIGMOD, pp. 593–604 (2007)Google Scholar
  10. 10.
    Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed temporal moving object clusters. In: Proc. of PVLDB, vol. 3(1-2), pp. 723–734 (2010)Google Scholar
  11. 11.
    Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. JIIS 27(3) (2006)Google Scholar
  12. 12.
    Panagiotakis, C., Pelekis, N., Kopanakis, I., Ramasso, E., Theodoridis, Y.: Segmentation and Sampling of Moving Object Trajectories based on Representativeness. In: TKDE (2011)Google Scholar
  13. 13.
    Parent, C., Spaccapietra, S., Renso, C., Andrienko, G., Andrienko, N., Bogorny, V., Damiani, M.L., Gkoulalas, D.A., Macedo, J.A., Pelekis, N., Theodoridis, Y., Yan, Z.: Semantic trajectories modeling and analysis. ACM Computing Surveys 35(4) (2013)Google Scholar
  14. 14.
    Pelekis, N., Kopanakis, I., Kotsifakos, E., Frentzos, E., Theodoridis, Y.: Clustering Uncertain Trajectories. KAIS 28(1), 117–147 (2011)Google Scholar
  15. 15.
    Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the generation of spatiotemporal datasets. In: Proc. of SSD (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nikos Pelekis
    • 1
  • Christos Ntrigkogias
    • 2
  • Panagiotis Tampakis
    • 2
  • Stylianos Sideridis
    • 2
  • Yannis Theodoridis
    • 2
  1. 1.Dept. of Statistics and Insurance ScienceUniv. of PiraeusGreece
  2. 2.Dept. of InformaticsUniv. of PiraeusGreece

Personalised recommendations