Abstract
The technological advances in smartphones and their widespread use has resulted in the big volume and varied types of mobile data which we have today. Location prediction through mobile data mining leverages such big data in applications such as traffic planning, location-based advertising, intelligent resource allocation; as well as in recommender services including the popular Apple Siri or Google Now. This paper, focuses on the challenging problem of predicting the next location of a mobile user given data on his or her current location. In this work, we propose NextLocation - a personalised mobile data mining framework - that not only uses spatial and temporal data but also other contextual data such as accelerometer, bluetooth and call/sms log. In addition, the proposed framework represents a new paradigm for privacy-preserving next place prediction as the mobile phone data is not shared without user permission. Experiments have been performed using data from the Nokia Mobile Data Challenge (MDC). The results on MDC data show large variability in predictive accuracy of about 17% across users. For example, irregular users are very difficult to predict while for more regular users it is possible to achieve more than 80% accuracy. To the best of our knowledge, our approach achieves the highest predictive accuracy when compared with existing results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Etter, V., Kafsi, M., Kazemi, E.: Been there, done that: What your mobility traces reveal about your behavior (2012)
Fox, V., Hightower, J., Liao, L., Schulz, D., Borriello, G.: Bayesian filtering for location estimation. IEEE Pervasive Computing 2(3), 24–33 (2003)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004)
Gama, J., Sebastiao, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 329–338. ACM, New York (2009)
Gao, H., Tang, J., Liu, H.: Mobile location prediction in spatio-temporal context
Gomes, J.B., Krishnaswamy, S., Gaber, M.M., Sousa, P.A., Menasalvas, E.: Mars: a personalised mobile activity recognition system. In: 2012 IEEE 13th International Conference on Mobile Data Management (MDM), pp. 316–319. IEEE (2012)
Gomes, J.B., Krishnaswamy, S., Gaber, M.M., Sousa, P.A.C., Menasalvas, E.: Mobile activity recognition using ubiquitous data stream mining. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 130–141. Springer, Heidelberg (2012)
Gomes, J.B., Menasalvas, E., Sousa, P.A.: Learning recurring concepts from data streams with a context-aware ensemble. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 994–999. ACM (2011)
Gomes, J.B., Sousa, P.A., Menasalvas, E.: Tracking recurrent concepts using context. Intelligent Data Analysis 16(5), 803–825 (2012)
Laurila, J.K., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O., Do, T.M.T., Dousse, O., Eberle, J., Miettinen, M.: The mobile data challenge: Big data for mobile computing research. In: Mobile Data Challenge by Nokia Workshop, in conjunction with Int. Conf. on Pervasive Computing, Newcastle, UK (2012)
Lu, Z., Zhu, Y., Zheng, V.W., Yang, Q.: Next place prediction by learning with multiple models
Mathew, W., Raposo, R., Martins, B.: Predicting future locations with hidden markov models (2012)
Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: Wherenext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637–646. ACM (2009)
Pan, R., Zhao, J., Zheng, V.W., Pan, J.J., Shen, D., Pan, S.J., Yang, Q.: Domain-constrained semi-supervised mining of tracking models in sensor networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1023–1027. ACM (2007)
Sherchan, W., Jayaraman, P.P., Krishnaswamy, S., Zaslavsky, A., Loke, S., Sinha, A.: Using on-the-move mining for mobile crowdsensing. In: 2012 IEEE 13th International Conference on Mobile Data Management (MDM), pp. 115–124. IEEE (2012)
Spaccapietra, S., Parent, C., Damiani, M.L., De Macedo, J.A., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data & Knowledge Engineering 65(1), 126–146 (2008)
Tran, L.H., Catasta, M., McDowell, L.K., Aberer, K.: Next place prediction using mobile data
Wang, J., Prabhala, B.: Periodicity based next place prediction
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Gomes, J.B., Phua, C., Krishnaswamy, S. (2013). Where Will You Go? Mobile Data Mining for Next Place Prediction. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-40131-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40130-5
Online ISBN: 978-3-642-40131-2
eBook Packages: Computer ScienceComputer Science (R0)