Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs

Sensors
Sungjun LeeKwanho Kim

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.

References

Sep 13, 2012·Sensors·Alicia Rodriguez-CarrionPatricia Noriega-Vivas
Nov 7, 2014·Sensors·Hao XiaYuanfei Chang

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Citations

Nov 20, 2019·Current Rheumatology Reports·Xiaojian JiFeng Huang

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Software Mentioned

cSPADE Discovery
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