Online Prediction of People’s Next Point-of-Interest: Concept Drift Support

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9277)


Current advances in location tracking technology provide exceptional amount of data about the users’ movements. The volume of geospatial data collected from moving users’ challenges human ability to analyze the stream of input data. Therefore, new methods for online mining of moving object data are required. One of the popular approaches available for moving objects is the prediction of the unknown future location of an object. In this paper we present a new method for online prediction of users’ next important locations to be visited that not only learns incrementally the users’ habits, but also detects and supports the drifts in their patterns. Our original contribution includes a new algorithm of online mining association rules that support the concept drift.


Mobile environment Human activities Activity prediction Online association rules Spatio-temporal data mining Concept drift 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.LIARA LaboratoryUniversity of Quebec at Chicoutimi (UQAC)ChicoutimiCanada
  2. 2.Tele-Universite of Quebec (TELUQ)QuebecCanada
  3. 3.University of SherbrookeSherbrookeCanada

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