Abstract
As mobile smart devices become ubiquitous in our society, users will be able to receive location based information on the fly. A model that is able to predict a user’s next destination will make location based services more effective by providing personalized information to the user. The implementation of such a location prediction model requires a set of correctly labeled destinations collected from users to tune the prediction model to an acceptable level of accuracy. A large collection of data will allow researchers to derive the parameters required to train predication models and also get the trends of user behaviors in general. ISL 3 will allow researchers to do just this by easily allowing them to collect user activity data to create location prediction models.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ashbrook, D., Staner, T.: Using GPS to Learn Significant Location and Predict Movement Across Multiple Users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)
Hariharam, R., Toyama, K.: Project Lachesis: parsing and Modeling Location Histories. In: Egenhofer, M.J., Freksa, C., Miller, H.J. (eds.) GIScience 2004. LNCS, vol. 3234, pp. 106–124. Springer, Heidelberg (2004)
Huang, Y., Griffin, T.: A Decision Tree Classification Model to Automate Trip Purpose Derivation. In: Computer Applications in Industry and Engineering (Proceedings), pp. 44–49 (2005)
Krumm, J.: Real Time Destination Prediction Based On Efficient Routes. Microsoft Research (2006)
Krumm, J., Horvitz, E.: The Microsoft Multiperson Location Survey (MSR -TR-2005-103). Microsoft Research (2005)
Laasonen, K.: Clustering and Prediction of Mobile User Routes from Cellular Data. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS, vol. 3721, pp. 569–576. Springer, Heidelberg (2005)
Laasonen, K., Raenato, M., Toivonen, H.: On-device Adaptive Location Recognition. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 287–304. Springer, Heidelberg (2004)
Raenato, M.: Mobile Communication and Context Dataset. Technical report. University of Helsinki (2004)
Cheng, C., Jain, R., van de Berg, E.: Location Prediction Algorithms for Mobile Wireless Systems. In: Wireless Internet Handbook: Technologies, Standards, and Applications, pp. 245–263. CRC Press, Boca Raton (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Huang, Y., Griffin, T., Lompo, S. (2009). Intelligent System for Locating, Labeling, and Logging (ISL 3). In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-92814-0_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92813-3
Online ISBN: 978-3-540-92814-0
eBook Packages: EngineeringEngineering (R0)