Path prediction through data mining

Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S. and Kalousis, A. (2007) Path prediction through data mining. In: IEEE International Conference on Pervasive Services, Istanbul, Turkey, 15-20 Jul 2007, pp. 128-135. ISBN 1424413257 (doi:10.1109/PERSER.2007.4283902)

Full text not currently available from Enlighten.

Publisher's URL: http://dx.doi.org/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.

Item Type:Conference Proceedings
Additional Information:ISBN: 1424413257
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos
Authors: Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S., and Kalousis, A.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Publisher:IEEE
ISBN:1424413257

University Staff: Request a correction | Enlighten Editors: Update this record