Research Article
Path Prediction through Data Mining
@INPROCEEDINGS{10.1109/PERSER.2007.4283902, author={Theodoros Anagnostopoulos and Christos B. Anagnostopoulos and Stathes Hadjiefthymiades and Alexandros Kalousis and Miltos Kyriakakos}, title={Path Prediction through Data Mining}, proceedings={1st International IEEE Conference on Pervasive Services}, publisher={IEEE}, proceedings_a={ICPS}, year={2007}, month={8}, keywords={data mining location prediction machine learning}, doi={10.1109/PERSER.2007.4283902} }- Theodoros Anagnostopoulos
Christos B. Anagnostopoulos
Stathes Hadjiefthymiades
Alexandros Kalousis
Miltos Kyriakakos
Year: 2007
Path Prediction through Data Mining
ICPS
IEEE
DOI: 10.1109/PERSER.2007.4283902
Abstract
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.
