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Querying and Reasoning for Spatiotemporal Data Mining

  • G. Manco
  • M. Baglioni
  • F. Giannotti
  • B. Kuijpers
  • A. Raffaetà
  • C. Renso
In the previous chapters, we studied movement data from several perspectives: the application opportunities, the type of analytical questions, the modeling requirements, and the challenges for mining. Moreover, the complexity of the overall analysis process was pointed out several times. The analytical questions posed by the end user need to be translated into several tasks such as choose analysis methods, prepare the data for application of these methods, apply the methods to the data, and interpret and evaluate the results obtained. To clarify these issues, let us consider an example involving the following analytical questions:
  • Describe the collective movement behavior of the population (or a given subset) of entiti es during the whole time period (or a given interval)

  • Find the entity subsets and time periods with the collective movement behavior corresponding to a given pattern

  • Compare the collective movement behaviors of the entities on given time intervals

It is evident that there is a huge distance between these analytical questions and the complex computations needed to answer them. In fact, answering the above questions requires combining several forms of knowledge and the cooperation among solvers of different nature: we need spatiotemporal reasoning supporting deductive inferences along with inductive mechanisms, in conjunction with statistical methods.

Keywords

Data Mining Association Rule Query Language Spatiotemporal Data Conjunctive Query 
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 2008

Authors and Affiliations

  • G. Manco
    • 1
  • M. Baglioni
    • 2
  • F. Giannotti
    • 3
  • B. Kuijpers
    • 4
  • A. Raffaetà
    • 5
  • C. Renso
    • 6
  1. 1.ICAR-CNRCosenzaItaly
  2. 2.KDD Laboratory, Dipartimento di InformaticaUniversità di PisaItaly
  3. 3.KDD LaboratoryISTI-CNRPisaItaly
  4. 4.Theoretical Computer Science GroupHasselt University and Transnational University of LimburgBelgium
  5. 5.Dipartimento di InformaticaUniversità Ca℉ Foscari di VeneziaItaly
  6. 6.KDD LaboratoryISTI-CNRPisaItaly

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