1st International ICST Conference on Autonomic Computing and Communication Systems

Research Article

Adaptive Learning of Semantic Locations and Routes

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  • @INPROCEEDINGS{10.4108/ICST.AUTONOMICS2007.2231,
        author={Keshu Zhang and Haifeng Li and Kari Torkkola and Mike Gardner},
        title={Adaptive Learning of Semantic Locations and Routes},
        proceedings={1st International ICST Conference on Autonomic Computing and Communication Systems},
        publisher={ICST},
        proceedings_a={AUTONOMICS},
        year={2007},
        month={10},
        keywords={},
        doi={10.4108/ICST.AUTONOMICS2007.2231}
    }
    
  • Keshu Zhang
    Haifeng Li
    Kari Torkkola
    Mike Gardner
    Year: 2007
    Adaptive Learning of Semantic Locations and Routes
    AUTONOMICS
    ICST
    DOI: 10.4108/ICST.AUTONOMICS2007.2231
Keshu Zhang1,*, Haifeng Li1, Kari Torkkola1, Mike Gardner1
  • 1: Motorola Labs 2900 S Diablo Way, Tempe, AZ 85282
*Contact email: keshu.zhang@motorola.com

Abstract

Adaptation of devices and applications based on contextual information has a great potential to enhance usability and mitigate the increasing complexity of mobile devices. An important topic in context-aware computing is to learn semantic locations and routes of mobile device users. Several batch methods have been proposed to learn these locations. However, such offline methods have very limited usefulness in practice. This paper describes an online adaptive approach to learn user’s semantic locations. The proposed method models user’s GPS data as a mixture of Gaussians, which is updated by an online estimation. The learned Gaussian mixture is then evaluated to determine which components most likely correspond to the important locations based on a priori probabilities. With learned semantic locations, we also propose a minimax criterion to discover user’s frequent transportation routes, which are modeled as sequences of GPS data. Finally, we describe an application of the proposed methods in a cell phone based automatic traffic alert system.