Path Prediction through Data Mining
Theodoros Anagnostopoulos, Christos B. Anagnostopoulos, Stathes P. Hadjiefthymiades, Alexandros Kalousis, Miltos Kyriakakos · 2007
Context-awareness is viewed as one of the most important aspects in the emerging ubiquitous computing paradigm. However, mobile applications are required to operate in pervasive computing environments of dynamic nature. Such applications predict the appropriate context in their environment in order to act efficiently. A context model, which deals with the location prediction of moving users, is proposed. Such model is used for trajectory classification through machine learning techniques. Hence, spatial and spatiotemporal context prediction is regarded as context classification based on supervised learning. Finally, two classification schemes are presented, evaluated and compared with other ML schemes in order to support location prediction and decision making.