GeoInformatica

, Volume 18, Issue 2, pp 273–312 | Cite as

A general framework for trajectory data warehousing and visual OLAP

  • Luca Leonardi
  • Salvatore Orlando
  • Alessandra Raffaetà
  • Alessandro Roncato
  • Claudio Silvestri
  • Gennady Andrienko
  • Natalia Andrienko
Article

Abstract

In this paper we present a formal framework for modelling a trajectory data warehouse (TDW), namely a data warehouse aimed at storing aggregate information on trajectories of moving objects, which also offers visual OLAP operations for data analysis. The data warehouse model includes both temporal and spatial dimensions, and it is flexible and general enough to deal with objects that are either completely free or constrained in their movements (e.g., they move along a road network). In particular, the spatial dimension and the associated concept hierarchy reflect the structure of the environment in which the objects travel. Moreover, we cope with some issues related to the efficient computation of aggregate measures, as needed for implementing roll-up operations. The TDW and its visual interface allow one to investigate the behaviour of objects inside a given area as well as the movements of objects between areas in the same neighbourhood. A user can easily navigate the aggregate measures obtained from OLAP queries at different granularities, and get overall views in time and in space of the measures, as well as a focused view on specific measures, spatial areas, or temporal intervals. We discuss two application scenarios of our TDW, namely road traffic and vessel movement analysis, for which we built prototype systems. They mainly differ in the kind of information available for the moving objects under observation and their movement constraints.

Keywords

Spatio-temporal data warehouses Visual analytics Distinct count problem 

Notes

Acknowledgements

This work has been partially supported by the national research project PON “TETRis” (no. PON01_00451), the Marie Curie Project SEEK (no. 295179) and the Cost Action MOVE (no. IC0903). We are grateful to our colleagues of the Department of Environmental Sciences for their support in the analysis of the vessels scenario. We thank the anonymous referees for their useful suggestions and Paolo Baldan for his careful reading of the paper.

References

  1. 1.
    Allen JF (1984) A general model of action and time. Artif Intell 23:123–154CrossRefGoogle Scholar
  2. 2.
    Andrienko G, Andrienko N (2008) Spatio-temporal aggregation for visual analysis of movements. In: Proceedings of VAST. IEEE. pp 51–58Google Scholar
  3. 3.
    Andrienko G, Andrienko N (2010) A general framework for using aggregation in visual exploration of movement data. Cartogr J 47(1):22–40Google Scholar
  4. 4.
    Andrienko G, Andrienko N, Wrobel S (2007) Visual analytics tools for analysis of movement data. ACM SIGKDD Explor 9(2):28–46CrossRefGoogle Scholar
  5. 5.
    Andrienko N, Andrienko G (2011) Spatial generalization and aggregation of massive movement data. IEEE Trans Vis Comput Graph 17(2):205–219CrossRefGoogle Scholar
  6. 6.
    Andrienko N, Andrienko G (2013) A visual analytics framework for spatio-temporal analysis and modelling. Data Min Knowl Disc 27(1):55–83Google Scholar
  7. 7.
    Andrienko N, Andrienko G (2013) Visual analytics of movement: an overview of methods, tools and procedures. Inf Vis 12(1):3–24CrossRefGoogle Scholar
  8. 8.
    Bimonte S, Miquel M (2010) When spatial analysis meets OLAP: multidimensional model and operators. IJDWM 6(4):33–60Google Scholar
  9. 9.
    Brakatsoulas S, Pfoser D, Salas R, Wenk C (2005) On map-matching vehicle tracking data. In: Proceedings of VLDB, pp 853–864Google Scholar
  10. 10.
    Brillinger D, Preisler H, Ager A, Kie K (2004) An exploratory data analysis (EDA) of the paths of moving animals. J Stat Plan Infer 122(2):43–63CrossRefGoogle Scholar
  11. 11.
    Cao H, Wolfson O, Trajcevski G (2006) Spatio-temporal data reduction with deterministic error bounds. VLDB J 15(3):211–228CrossRefGoogle Scholar
  12. 12.
    Cudré-Mauroux P, Wu E, Madden S (2010) TrajStore: an adaptive storage system for very large trajectory data sets. In: Proceedings of ICDE, pp 109–120Google Scholar
  13. 13.
    Dykes JA, Mountain DM (2003) Seeking structure in records of spatio-temporal behaviour: visualization issues, efforts and applications. Comput Stat Data Anal 43(4):581–603CrossRefGoogle Scholar
  14. 14.
    Egenhofer MJ (1994) Topological relations between regions with holes. Int J GIS 8:129–142Google Scholar
  15. 15.
    Eick SG (2000) Visualizing multi-dimensional data. SIGGRAPH Comput Graph 34:61–67CrossRefGoogle Scholar
  16. 16.
    Erwig M, Güting RH, Schneider M, Vazirgiannis M (1999) Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica 3(3):269–296CrossRefGoogle Scholar
  17. 17.
    Forer P, Huisman O (2000) Information, place and cyberspace: issues in accessibility, chap. time and sequencing: substitution at the physical/virtual interface. Springer Verlag, Heidelberg, pp 73–90Google Scholar
  18. 18.
    Fredrikson A, North C, Plaisant C, Shneiderman B (1999) Temporal, geographical and categorical aggregations viewed through coordinated displays: a case study with highway incident data. In: Proceedings of workshop on new paradigms in information visualization and manipulation, pp 26–34Google Scholar
  19. 19.
    European project IST-6FP-014915 GeoPKDD Geographic privacy-aware knowledge discovery and delivery (GeoPKDD) (web site: http://www.geopkdd.eu)
  20. 20.
    Golfarelli M, Maio D, Rizzi S (1998) The dimensional fact model: a conceptual model for data warehouses. Int J Coop Inf Syst 7(2–3):215–247CrossRefGoogle Scholar
  21. 21.
    Gómez LI, Haesevoets S, Kuijpers B, Vaisman AA (2009) Spatial aggregation: data model and implementation. Inf Syst 34(6):551–576CrossRefGoogle Scholar
  22. 22.
    Gómez LI, Kuijpers B, Vaisman AA (2011) A data model and query language for spatio-temporal decision support. GeoInformatica 15(3):455–496CrossRefGoogle Scholar
  23. 23.
    Gray J, Chaudhuri S, Bosworth A, Layman A, Reichart D, Venkatrao M, Pellow F, Pirahesh H (1997) Data cube: a relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Min Knowl Disc 1(1):29–53CrossRefGoogle Scholar
  24. 24.
    Guo D (2007) Visual analytics of spatial interaction patterns for pandemic decision support. Int J Geograph Inf Sci 21:859–877CrossRefGoogle Scholar
  25. 25.
    Güting RH, de Almeida VT, Ding Z (2006) Modeling and querying moving objects in networks. VLDB J 15(2):165–190CrossRefGoogle Scholar
  26. 26.
    Güting RH, Behr T, Düntgen C (2010) SECONDO: a platform for moving objects database research and for publishing and integrating research implementations. IEEE Data Eng Bull 33(2):56–63Google Scholar
  27. 27.
    Güting RH, Schneider M (2005) Moving Objects Databases. Morgan Kaufman, San MateoGoogle Scholar
  28. 28.
    Han J, Stefanovic N, Kopersky K (1998) Selective materialization: an efficient method for spatial data cube construction. In: Proceedings of PAKDD, pp 144–158Google Scholar
  29. 29.
    Jensen CS, Kligys A, Pedersen TB, Timko I (2004) Multidimensional data modeling for location-based services. VLDB J 13(1):1–21CrossRefGoogle Scholar
  30. 30.
    Jensen CS, Lu H, Yang B (2009) Indexing the trajectories of moving objects in symbolic indoor space. In: Proceedings of SSTD, pp 208–227Google Scholar
  31. 31.
    Keim D, Kohlhammer J, Ellis G, Mansmann F (eds) (2010) Mastering the information age solving problems with visual analytics, chap. data mining, pp 39–56. Eurographics AssociationGoogle Scholar
  32. 32.
    Leonardi L, Orlando S, Raffaetà A, Roncato A, Silvestri C (2009) Frequent spatio-temporal patterns in trajectory data warehouses. In: Proceedings of SAC. ACM, pp 1433–1440Google Scholar
  33. 33.
    Liu K, Deng K, Ding Z, Li M, Zhou X (2009) MOIR/MT: monitoring large-scale road network traffic in real-time. PVLDB 2(2):1538–1541Google Scholar
  34. 34.
    Malinowski E, Zimányi E (2004) Representing spatiality in a conceptual multidimensional model. In: Proceedings of GIS, pp 12–22Google Scholar
  35. 35.
    Malinowski E, Zimányi E (2007) Logical representation of a conceptual model for spatial data warehouses. GeoInformatica 11(4):431–457CrossRefGoogle Scholar
  36. 36.
    Marketos G, Frentzos E, Ntoutsi I, Pelekis N, Raffaetà A, Theodoridis Y (2008) Building Real World Trajectory Warehouses. In: Proceedings of MobiDE, pp 8–15Google Scholar
  37. 37.
    Orlando S, Orsini R, Raffaetà A, Roncato A, Silvestri C (2007) Trajectory data warehouses: design and implementation issues. J Comput Sci Eng 1(2):240–261CrossRefGoogle Scholar
  38. 38.
    Papadias D, Tao Y, Kalnis P, Zhang J (2002) Indexing spatio-temporal data warehouses. In: Proceedings of ICDE, pp 166–175Google Scholar
  39. 39.
    Papadias D, Zhang J, Mamoulis N, Tao Y (2003) Query processing in spatial network databases. In: Proceedings of VLDB, pp 802–813Google Scholar
  40. 40.
    Pedersen T, Tryfona N (2001) Pre-aggregation in spatial data warehouses. In: Proceedings of SSTD, LNCS, vol 2121, pp 460–480Google Scholar
  41. 41.
    Pelekis N, Theodoridis Y (2006) Boosting location-based services with a moving object database engine. In: Proceedings of MobiDE, pp 3–10Google Scholar
  42. 42.
    Pfoser D, Jensen CS (2005) Trajectory indexing using movement constraints. GeoInformatica 9(2):93–115CrossRefGoogle Scholar
  43. 43.
    Popa IS, Zeitouni K, Oria V, Barth D, Vial S (2011) Indexing in-network trajectory flows. VLDB J 20(5):643–669CrossRefGoogle Scholar
  44. 44.
    Raffaetà A, Leonardi L, Marketos G, Andrienko G, Andrienko N, Frentzos E, Giatrakos N, Orlando S, Pelekis N, Roncato A, Silvestri C (2011) Visual mobility analysis using T-warehouse. IJDWM 7(1):1–23Google Scholar
  45. 45.
    Sakr M, Andrienko G, Behr T, Andrienko N, Güting RH, Hurter C (2011) Exploring spatiotemporal patterns by integrating visual analytics with a moving objects database system. In: Proceedings of GIS, pp 505–508Google Scholar
  46. 46.
    Sakr MA, Güting RH (2011) Spatiotemporal pattern queries. GeoInformatica 15(3):497–540CrossRefGoogle Scholar
  47. 47.
    Siqueira TLL, de Aguiar Ciferri CD, Times VC, Ciferri RR (2012) The SB-index and the HSB-index: efficient indices for spatial data warehouses. GeoInformatica 16(1):165–205CrossRefGoogle Scholar
  48. 48.
    Stolte C, Tang D, Hanrahan P (2002) Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans Vis Comput Graph 8:52–65CrossRefGoogle Scholar
  49. 49.
    Sweeney L (2002) Achieving k-anonymity privacy protection using generalization and suppression. Int J Uncertain Fuzz Knowl-Based Syst 10:571–588CrossRefGoogle Scholar
  50. 50.
    Tao Y, Kollios G, Considine J, Li F, Papadias D (2004) Spatio-temporal aggregation using sketches. In: Proceedings of ICDE, pp 214–225Google Scholar
  51. 51.
    Tao Y, Papadias D (2005) Historical spatio-temporal aggregation. ACM TOIS 23:61–102CrossRefGoogle Scholar
  52. 52.
    Timko I, Pedersen TB (2004) Capturing complex multidimensional data in location-based data warehouses. In: Proceedings of GIS, pp 147–156Google Scholar
  53. 53.
    Veilleux JP, Lambert M, Santerre R, B\(\acute{\rm e}\)dard Y (2004) Utilisation du syst\(\acute{\rm e}\)me de positionnement par satellites (GPS) et des outils d’exploration et d’analyse SOLAP pour l’\(\acute{\rm e}\)valuation et le suivi de sportifs de haut niveau. In: Colloque G\(\acute{\rm e}\)omatique 2004 – Un choix strat\(\acute{\rm e}\)gique!Google Scholar
  54. 54.
    Wan T, Zeitouni K, Meng X (2007) An OLAP system for network-constrained moving objects. In: Proceedings of SAC, pp 13–18Google Scholar
  55. 55.
    Wolfson O, Xu B, Chamberlain S, Jiang L (1998) Moving objects databases: issues and solutions. In: Proceedings of SSDBM, pp 111–122Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Luca Leonardi
    • 1
  • Salvatore Orlando
    • 1
  • Alessandra Raffaetà
    • 1
  • Alessandro Roncato
    • 1
  • Claudio Silvestri
    • 1
  • Gennady Andrienko
    • 2
  • Natalia Andrienko
    • 2
  1. 1.DAISUniversità Ca’ Foscari VeneziaVeneziaItaly
  2. 2.IAISFraunhofer InstituteSankt AugustinGermany

Personalised recommendations