Human Behavior Understanding pp 97-116
Online Prediction of People’s Next Point-of-Interest: Concept Drift Support
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.