The VLDB Journal

, 18:1335 | Cite as

BerlinMOD: a benchmark for moving object databases

  • Christian Düntgen
  • Thomas Behr
  • Ralf Hartmut Güting
Regular Paper

Abstract

This document presents a method to design scalable and representative moving object data (MOD) and two sets of queries for benchmarking spatio-temporal DBMS. Instead of programming a dedicated generator software, we use the existing Secondo DBMS to create benchmark data. The benchmark is based on a simulation scenario, where the positions of a sample of vehicles are observed for an arbitrary period of time within the street network of Berlin. We demonstrate the data generator’s extensibility by showing how to achieve more natural movement generation patterns, and how to disturb the vehicles’ positions to create noisy data. As an application and for reference, we also present first benchmarking results for the Secondo DBMS. Whereas the benchmark focuses on range queries, we demonstrate its ability to incorporate new future classes of queries by presenting a preliminary extension handling various nearest neighbour queries. Such a benchmark is useful in several ways: It provides well-defined data sets and queries for experimental evaluations; it simplifies experimental repeatability; it emphasizes the development of complete systems; it points out weaknesses in existing systems motivating further research. Moreover, the BerlinMOD benchmark allows one to compare different representations of the same moving objects.

Keywords

Benchmark Moving object database Data generator Spatio-temporal database Trajectory 

References

  1. 1.
    Barioni, M.C.N., Razente, H., Traina, A., Caetano Traina, J.: Siren: a similarity retrieval engine for complex data. In: VLDB ’06: Proceedings of the 32nd International Conference on Very large Data Bases, pp. 1155–1158. VLDB Endowment (2006)Google Scholar
  2. 2.
    Benetis, R., Jensen, C.S., Karciauskas, G., Saltenis, S.: Nearest neighbor and reverse nearest neighbor queries for moving objects. In: IDEAS ’02: Proceedings of the 2002 International Symposium on Database Engineering & Applications, pp. 44–53. IEEE Computer Society, Washington, DC (2002)Google Scholar
  3. 3.
  4. 4.
    Böhm, C., Ooi, B.C., Plant, C., Yan, Y.: Efficiently processing continuous k-NN queries on data streams. In: ICDE, pp. 156–165 (2007)Google Scholar
  5. 5.
    Brinkhoff T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)MATHCrossRefGoogle Scholar
  6. 6.
    Brinkhoff, T.: Geodatenbanksysteme in Theorie und Praxis. Wichmann Verlag, (2005)Google Scholar
  7. 7.
    de Almeida V.T., Güting R.H.: Indexing the trajectories of moving objects in networks. Geoinformatica 9(1), 33–60 (2005)CrossRefGoogle Scholar
  8. 8.
    de Almeida, V.T., Güting, R.H., Düntgen, C.: Multiple entry indexing and double indexing. In: IDEAS 2007, pp. 181–189. IEEE Computer Society (2007)Google Scholar
  9. 9.
    Dieker, S., Güting, R.H.: Plug and play with query algebras: SECONDO—a generic DBMS development environment. In: Proceedings of the International Symposium on Database Engineering and Applications, pp. 380–392 (2000)Google Scholar
  10. 10.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. In: Numerische Mathematik, vol. 1, pp. 269–271. Mathematisch Centrum, Amsterdam, The Netherlands (1959)Google Scholar
  11. 11.
    Ding, Z., Güting, R.H.: Managing moving objects on dynamic transportation networks. In: SSDBM, pp. 287–296 (2004)Google Scholar
  12. 12.
    Erwig M., Güting R.H., Schneider M., Vazirgiannis M.: Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica 3(3), 269–296 (1999)CrossRefGoogle Scholar
  13. 13.
    ESRI, Shapefile Technical Description (1998)Google Scholar
  14. 14.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proceedings 1994 ACM SIGMOD Conference, Mineapolis, pp. 419–429 (1994)Google Scholar
  15. 15.
    Forlizzi, L., Güting, R.H., Nardelli, E., Schneider, M.: A data model and data structures for moving objects databases. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 319–330. ACM Press, New York (2000)Google Scholar
  16. 16.
    Frentzos E., Gratsias K., Pelekis N., Theodoridis Y.: Algorithms for nearest neighbor search on moving object trajectories. Geoinformatica 11(2), 159–193 (2007)CrossRefGoogle Scholar
  17. 17.
    Gao, Y.-J., Li, C., Chen, G.-C., Chen, L., Jiang, X.-T., Chen, C.: Efficient k-nearest-neighbor search algorithms for historical moving object trajectories. J. Comput. Sci. Technol. 232–244 (2007)Google Scholar
  18. 18.
    Giannotti, F., Mazzoni, A., Puntoni, S., Renso, C.: Synthetic generation of cellular network positioning data. In: GIS ’05: Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems, pp. 12–20. ACM Press, New York (2005)Google Scholar
  19. 19.
    Gidófalvi, G., Pedersen, T.B.: ST-ACTS: A spatio-temporal activity simulator. In: de By, R.A., Nittel, S., (eds.), GIS, pp. 155–162. ACM Press, New York (2006)Google Scholar
  20. 20.
    Güting, R.H.: Second-order signature: a tool for specifying data models, query processing, and optimization. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 277–286 (1993)Google Scholar
  21. 21.
    Güting R.H., Böhlen M.H., Erwig M., Jensen C.S., Lorentzos N.A., Schneider M., Vazirgiannis M.: A foundation for representing and quering moving objects. ACM Trans. Database Syst. 25(1), 1–42 (2000)CrossRefGoogle Scholar
  22. 22.
    Güting, R.H., de Almeida, V.T., Ansorge, D., Behr, T., Ding, Z., Höse, T., Hoffmann, F., Spiekermann, M., Telle, U.: Secondo: An extensible DBMS platform for research prototyping and teaching. In: ICDE, pp. 1115–1116 (2005)Google Scholar
  23. 23.
    Güting R.H., de Almeida V.T., Ding Z.: Modeling and querying moving objects in networks. VLDB J. 15(2), 165–190 (2006)CrossRefGoogle Scholar
  24. 24.
    Hadjieleftheriou, M., Kollios, G., Gunopulos, D., Tsotras, V.: Line discovery of dense areas in spatio-temporal databases (2003)Google Scholar
  25. 25.
    Hu, H., Xu, J., Lee, D.L.: A generic framework for monitoring continuous spatial queries over moving objects. In: SIGMOD ’05: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 479–490. ACM Press, New York (2005)Google Scholar
  26. 26.
    Jensen, C.S., Lin, D., Ooi, B.C., Zhang, R.: Effective density queries on continuously moving objects. In: International Conference on Data Engineering (ICDE), p. 71 (2006)Google Scholar
  27. 27.
    Jensen, C.S., Tiesyte, D., Tradisauskas, N.: The COST benchmark—comparison and evaluation of spatio-temporal indexes. In: DASFAA, pp. 125–140 (2006)Google Scholar
  28. 28.
    Jeong, S.-H., Fernandes, A.A.A., Paton, N.W., Griffiths, T.: A generic algorithmic framework for aggregation of spatio-temporal data. In: SSDBM ’04: Proceedings of the 16th International Conference on Scientific and Statistical Database Management, p. 245. IEEE Computer Society, Washington (2004)Google Scholar
  29. 29.
    Korn F., Muthukrishnan S.: Influence sets based on reverse nearest neighbor queries. SIGMOD Rec. 29(2), 201–212 (2000)CrossRefGoogle Scholar
  30. 30.
    Krajzewicz, D., Hertkorn, G., Rössel, C., Wagner, P.: SUMO (Simulation of Urban MObility): an open-source traffic simulation. In: Proceedings of the 4th Middle East Symposium on Simulation and Modelling (MESM2002), pp. 183–187. SCS European Publishing House (2002)Google Scholar
  31. 31.
    Lang, C.A., Singh, A.K.: A framework for accelerating high-dimensional NN-queries, TRCS01-04. Department of Computer Science, University of California, Santa Barbara (2001)Google Scholar
  32. 32.
    Lema, J.A.C., Behr, T.: External representation of spatial and spatio-temporal values. http://dna.fernuni-hagen.de/Secondo.html/files/SpatialListFormat.pdf (2004)
  33. 33.
    Mokbel M.F., Ghanem T.M., Aref W.G.: Spatio-temporal access methods. IEEE Data Eng. Bull. 26(2), 40–49 (2003)Google Scholar
  34. 34.
    Mouratidis K., Papadias D.: Continuous nearest neighbor queries over sliding windows. IEEE Trans. Knowl. Data Eng. 19(6), 789–803 (2007)CrossRefGoogle Scholar
  35. 35.
    Mouratidis, K., Papadias, D., Hadjieleftheriou, M.: Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: SIGMOD ’05: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 634–645. ACM Press, New YorkGoogle Scholar
  36. 36.
    Myllymaki, J., Kaufman, J.: Dynamark: a benchmark for dynamic spatial indexing. In: MDM ’03: Proceedings of the 4th International Conference on Mobile Data Management, pp. 92–105. Springer, London (2003)Google Scholar
  37. 37.
    Papadias D., Tao Y., Mouratidis K., Hui C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. 30(2), 529–576 (2005)CrossRefGoogle Scholar
  38. 38.
    Pelekis, N., Frentzos, E., Giatrakos, N., Theodoridis, Y.: HERMES: Aggregative LBS via a trajectory DB engine. In: SIGMOD ’08: Proceedings of the 2008 ACM SIGMOD International Conference On Management Of Data, pp. 1255–1258. ACM Press, New York (2008)Google Scholar
  39. 39.
    Pelekis, N., Theodoridis, Y.: Boosting location-based services with a moving object database engine. In: MobiDE ’06: Proceedings of the 5th ACM International Workshop on Data Engineering For Wireless and Mobile Access, pp. 3–10. ACM Press, New York (2006)Google Scholar
  40. 40.
    Pelekis, N., Theodoridis, Y.: An oracle data cartridge for moving objects. Technical Report UNIPI-ISL-TR-2007-04, Information Systems Lab., University of Piraeus, Piraeus, Hellas, December 2007Google Scholar
  41. 41.
    Pfoser D., Theodoridis Y.: Generating semantics-based trajectories of moving objects. Comput. Environ. Urban Syst. 27(3), 243–263 (2003)CrossRefGoogle Scholar
  42. 42.
    Rezić, S.: http://bbbike.de (2008)
  43. 43.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD ’95: Proceedings of the 1995 ACM SIGMOD, International Conference on Management of Data, pp. 71–79. ACM Press, New York (1995)Google Scholar
  44. 44.
    Saglio J.-M., Moreira J.: Oporto: a realistic scenario generator for moving objects. Geoinformatica 5(1), 71–93 (2001)MATHCrossRefGoogle Scholar
  45. 45.
  46. 46.
    Singh, A., Ferhatosmanoglu, H., Tosun, A.Ş.: High dimensional reverse nearest neighbor queries. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, New Orleans, LA, USA, pp. 91–98. ACM, New York (2003). doi:10.1145/956863.956882. ISBN 1-58113-723-0
  47. 47.
    Sistla A.P., Wolfson O., Chamberlain S., Dao S.: Querying the uncertain position of moving objects. Lect. Notes Comput. Sci. 1399, 310–337 (1998)CrossRefGoogle Scholar
  48. 48.
    SMARTEST. Final Report for Publication. Technical report, European Commission Transport RTD Programme of the 4th Framework Programme, 1999, Project Reference: RO-97-SC.1059. http://www.its.leeds.ac.uk/projects/smartest/finrep.PDF
  49. 49.
    SMARTEST Project Web Site. http://www.its.leeds.ac.uk/projects/smartest/ (1999)
  50. 50.
    Song, Z., Roussopoulos, N.: K-nearest neighbor search for moving query point. In: SSTD ’01: Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, pp. 79–96. Springer, London (2001)Google Scholar
  51. 51.
    Stanoi, I., Agrawal, D., Abbadi, A.E.: Reverse nearest neighbor queries for dynamic databases. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 44–53 (2000)Google Scholar
  52. 52.
    Statistisches Landesamt Berlin. Bevölkerungsstand in Berlin Ende September 2006 nach Bezirken (2008)Google Scholar
  53. 53.
    Statistisches Landesamt Berlin. Interaktiver Stadtatlas: http://www.statistik-berlin.de/framesets/berl.htm (2008)
  54. 54.
    Stonebraker, M., Frew, J., Gardels, K., Meredith, J.: The Sequoia 2000 Benchmark. In: SIGMOD Conference, pp. 2–11 (1993)Google Scholar
  55. 55.
    Tao, Y., Papadias, D., Lian, X.: Reverse knn search in arbitrary dimensionality. In: VLDB ’04: Proceedings of the Thirtieth international conference on Very large data bases, pp. 744–755. VLDB Endowment (2004)Google Scholar
  56. 56.
    Theodoridis Y.: Ten benchmark database queries for location-based services. Comput. J. 46(6), 713–725 (2003)MATHCrossRefGoogle Scholar
  57. 57.
    Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the generation of spatiotemporal datasets. In: SSD, pp. 147–164 (1999)Google Scholar
  58. 58.
    Tzouramanis T., Vassilakopoulos M., Manolopoulos Y.: On the generation of time-evolving regional data. Geoinformatica 6(3), 207–231 (2002). doi:10.1023/A:1019705618917 ISSN 1384-6175MATHCrossRefGoogle Scholar
  59. 59.
    Tzouramanis T., Vassilakopoulos M., Manolopoulos Y.: Benchmarking access methods for time-evolving regional data. Data Knowl. Eng. 49(3), 243–286 (2004)CrossRefGoogle Scholar
  60. 60.
    Vazirgiannis, M., Wolfson, O.: A spatiotemporal model and language for moving objects on road networks. In: SSTD ’01: Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases, pp. 20–35. Springer, London (2001)Google Scholar
  61. 61.
    Werstein, P.: A performance benchmark for spatiotemporal databases. In: Tenth Annual Colloquium of the Spatial Information Research Centre, 16–19 Dec, Dunedin, New Zealand, pp. 1365–1374. University of Otago (1998)Google Scholar
  62. 62.
    Wolfson, O., Chamberlain, S., Dao, S., Jiang, L., Mendez, G.: Cost and imprecision in modeling the position of moving objects. In: ICDE, pp. 588–596 (1998)Google Scholar
  63. 63.
    Wolfson O., Sistla A.P., Chamberlain S., Yesha Y.: Updating and querying databases that track mobile units. Distrib. Parallel Databases 7(3), 257–387 (1999)CrossRefGoogle Scholar
  64. 64.
    Wolfson, O., Xu, B., Chamberlain, S., Jiang, L.: Moving objects databases: issues and solutions. In: Statistical and Scientific Database Management, pp. 111–122 (1998)Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Christian Düntgen
    • 1
  • Thomas Behr
    • 1
  • Ralf Hartmut Güting
    • 1
  1. 1.Faculty of Mathematics and Computer ScienceUniversity of HagenHagenGermany

Personalised recommendations