Statistical methods for speech recognition
Statistical methods for speech recognition
An experimental comparison of model-based clustering methods
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Journal of Machine Learning Research
Model-based document clustering with a collapsed gibbs sampler
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate GSM indoor localization
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Practical metropolitan-scale positioning for GSM phones
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
FindingMiMo: tracing a missing mobile phone using daily observations
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Mobility prediction-based smartphone energy optimization for everyday location monitoring
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
SSN: a seamless spontaneous network design around opportunistic contacts
Journal of Mobile Multimedia
Energy efficient continuous location determination for pedestrian information systems
MobiDE '12 Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Personalized behavior pattern recognition and unusual event detection for mobile users
Mobile Information Systems
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Much research focuses on predicting a person's geo-spatial traversal patterns using a history of recorded geo-coordinates. In this paper, we focus on the problem of predicting location-state transitions. Location-states for a user refer to a set of anchoring points/regions in space, and the prediction task produces a sequence of predicted location states for a given query time window. If this problem can be solved accurately and efficiently, it may lead to new location based services (LBS) that can smartly recommend information to a user based on his current and future location states. The proposed iLoc (Incremental (Location-State Acquisition and Prediction) framework solves the prediction problem by utilizing the sensor information provided by a user's mobile device. It incrementally learns the location states by constantly monitoring the signal environment of the mobile device. Further, the framework tightly integrates the learning and prediction modules, allowing iLoc to update location-states continuously and predict future location-states at the same time. Our extensive experiments show that the quality of the location-states learned by iLoc are better than the state-of-the-art. We also show that when other learners failed to produce reasonable predictions, iLoc provides good forecasts. As for the efficiency, iLoc processes the data in a single pass, which fits well to many data stream processing models.