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NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems

  • Salvatore Scellato
  • Mirco Musolesi
  • Cecilia Mascolo
  • Vito Latora
  • Andrew T. Campbell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6696)

Abstract

Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More specifically, they are able to only forecast the next location of a user, but not his/her arrival time and residence time, i.e., the interval of time spent in that location. Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future.

In this paper we present NextPlace, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places. NextPlace focuses on the predictability of single users when they visit their most important places, rather than on the transitions between different locations. We report about our evaluation using four different datasets and we compare our forecasting results to those obtained by means of the prediction techniques proposed in the literature. We show how we achieve higher performance compared to other predictors and also more stability over time, with an overall prediction precision of up to 90% and a performance increment of at least 50% with respect to the state of the art.

Keywords

Time Series Access Point Location Prediction Prediction Technique Nonlinear Time Series 
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 2011

Authors and Affiliations

  • Salvatore Scellato
    • 1
  • Mirco Musolesi
    • 2
  • Cecilia Mascolo
    • 1
  • Vito Latora
    • 3
  • Andrew T. Campbell
    • 4
  1. 1.Computer LaboratoryUniversity of CambridgeUK
  2. 2.School of Computer ScienceUniversity of St. AndrewsUK
  3. 3.Dipartimento di FisicaUniversity of CataniaItaly
  4. 4.Department of Computer ScienceDartmouth CollegeUSA

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