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Spatiotemporal Data Indexing

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Spatiotemporal data in its most general form consists of observations with their ... The main problem towards modeling spatiotemporal data is that it needs to be ... – PowerPoint PPT presentation

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Title: Spatiotemporal Data Indexing


1
Spatiotemporal Data Indexing
  • Presented by Slobodan Rasetic
  • University of Alberta

2
Spatiotemporal Data
  • Spatiotemporal data in its most general form
    consists of observations with their timestamps
    and spatial location.
  • Trajectories are often used to represent moving
    objects.
  • In this case, the positions of moving objects are
    recorded at discrete points in time, and a linear
    interpolation between two successive locations is
    typically assumed
  • Practical examples of useful spatio-temporal
    data include GPS, wireless networks, and sensor
    data.

3
Spatiotemporal Data (cont)
Moving object trajectory Pfo00
Moving object trajectories Pfo00
4
Modeling Spatiotemporal Data
  • The main problem towards modeling spatiotemporal
    data is that it needs to be modeled in a manner
    which best supports a particular user query.
  • There are several types of users queries that can
    be posed over spatiotemporal data.
  • Examples of such include range queries and
    nearest neighbor queries.

5
R-Trees
  • R-Tree based structures provide a good mechanism
    for supporting a wide range of query types for
    Spatiotemporal data.
  • Spatial-temporal data, like spatial data can be
    approximated using a Minimum Bounding Rectangle
    (MBB).

Approximation of Spatial Data Gut84
A resulting R-Tree Structure Gut84
6
R-Trees (Cont)
  • Approximating trajectories using MBBs and using
    an R-Tree based structure for query support has
    the following benefits
  • 1. Low storage overhead.
  • 2. Low computational overhead to answer user
    queries.
  • Approximating trajectories using R-Tree based
    structures has the drawback of introducing a
    great deal of dead space.
  • This dead space can be reduced by dividing
    trajectories into smaller segments and
    approximating their sub components.

7
Trajectory Approximation
  • Using courser approximations for trajectories has
    the obvious benefit of using less dead space.
  • Too much dead space leads to poor query
    performance, i.e. query miss hits.

Reducing the dead space occupied by a trajectory
Pfo00
8
Query Performance
  • Splitting a trajectory without reference to a
    particular query size might also lead to poor
    query performance.
  • The following examples helps to describe why this
    is true.

9
References
  • Gut84 GUTTMAN, A. R-trees a Dynamic Index
    Structure for Spatial Searching. In Proc. of
    ACM-SIGMOD Conference on the Management of Data,
    pp. 47-57, 1984.
  • Pfo00 PFOSER, D., JENSEN, C. S., AND
    THEODORIDIS, Y.Novel Approaches in Query
    Processing for Moving Object Trajectories. In
    Proceedings of the 26st VLDB Conf. (Cairo,Egypt,
    September 2000), pp. 395406.

10
Questions and Discussion??
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