Trajectory Data Warehouses

  • Alejandro Vaisman
  • Esteban Zimányi
Part of the Data-Centric Systems and Applications book series (DCSA)


The previous chapter focused on the analysis of the spatial features of static objects such as stores, cities, or states, where by static we mean that the spatial features of these objects do not change (or change exceptionally) across time. However, there is a wide range of applications that require the analysis of the so-called moving objects, that is, objects that continuously change their position in space and time. This is called mobility data analysis. The interest in mobility data analysis has expanded dramatically with the availability of embedded positioning devices like GPS. With these devices, traffic data, for example, can be captured as a collection of sequences of positioning signals transmitted by the cars’ GPS along their itineraries. Since such sequences can be very long, they are often processed by dividing them in segments.


Data Warehouse Temporal Type Aggregation Operation Temporal Real Valid Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alejandro Vaisman
    • 1
  • Esteban Zimányi
    • 2
  1. 1.Instituto Tecnológico de Buenos AiresBuenos AiresArgentina
  2. 2.Université Libre de BruxellesBrusselsBelgium

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