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Applying Data Mining in Urban Environments Using the Roles Model Approach

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Advances in Artificial Intelligence -- IBERAMIA 2014 (IBERAMIA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8864))

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Abstract

One of the main challenges in urban computing is to interpret the behaviors of the individuals, and so to provide services for suppling their needs. Data mining offers very powerful tools that can be used to analyze data in urban environments. Our research uses the UrbanContext roles model to identify the states of the individuals within urban environments. Then, it applies supervised classification data mining techniques to the results obtained, and uses decision trees in order to facilitate the analysis of the individuals’ behavior. Finally, we present the prediction results obtained from a study made about the roles that individuals adopt depending on their context. From these data we successfully predict the different types of services we can offer in an urban environment.

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Author information

Authors and Affiliations

  1. Information Technologies Group GTI, Department of Telematics Engineering, University of Vigo, Vigo, Spain

    Claudia Liliana Zúñiga-Cañón & Juan Carlos Burguillo

  2. Research Group COMBA R & D, Department of Engineering, University of Santiago de Cali, Cali, Colombia

    Claudia Liliana Zúñiga-Cañón

Authors
  1. Claudia Liliana Zúñiga-Cañón
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  2. Juan Carlos Burguillo
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Corresponding author

Correspondence to Claudia Liliana Zúñiga-Cañón .

Editor information

Editors and Affiliations

  1. Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Ana L.C. Bazzan

  2. Pontifica Universidad Católica (PUC), Santiago de Chile, Chile

    Karim Pichara

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Zúñiga-Cañón, C.L., Burguillo, J.C. (2014). Applying Data Mining in Urban Environments Using the Roles Model Approach. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_56

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  • DOI: https://doi.org/10.1007/978-3-319-12027-0_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12026-3

  • Online ISBN: 978-3-319-12027-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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