%0 Journal Article %1 lee2016next %A Lee, S %A Lim, J %A Park, J %A Kim, K %D 2016 %J Sensors (Basel) %K device diss geo inthesis log mining mobile next place prediction spatial temporal %N 2 %P 145-145 %R 10.3390/s16020145 %T Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs %U http://europepmc.org/abstract/med/26805850 %V 16 %X Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user's next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user's past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user's next places than the previous approaches considered in most cases.

@article{lee2016next, abstract = {Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user's next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user's past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user's next places than the previous approaches considered in most cases.}, added-at = {2017-02-04T22:43:41.000+0100}, author = {Lee, S and Lim, J and Park, J and Kim, K}, biburl = {https://www.bibsonomy.org/bibtex/2337a142b994c39da32c9b6b5f3924839/becker}, doi = {10.3390/s16020145}, interhash = {59eccca1783d2f19e235ad121c21969e}, intrahash = {337a142b994c39da32c9b6b5f3924839}, journal = {Sensors (Basel)}, keywords = {device diss geo inthesis log mining mobile next place prediction spatial temporal}, month = jan, number = 2, pages = {145-145}, pmid = {26805850}, timestamp = {2017-02-04T22:43:41.000+0100}, title = {Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs}, url = {http://europepmc.org/abstract/med/26805850}, volume = 16, year = 2016 }