The increasingly extensive availability of location- acquisition technologies (such as GPS and GSM networks) and mobile computing techniques have generated a lot of spatial-temporal trajectory data which represents the mobility of diversification of moving objects such as people, vehicles, and animals. This brings new opportunities to understand the movement behaviors of moving objects in mobile social networks, which can become valuable for location-based services (LBS). However, the explosive growth of users' trajectory data has brought a lot of troubles to online real-time processing. In this paper, we focus on this direction and develop a complete data-driven framework involving real-time user mobility pattern modeling and a novel user similarity measurement based on both spatial and temporal information. Through experimental evaluation, it is verified that the proposed mobility pattern modeling method and similarity measurement can deliver excellent performance.


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    Title :

    A Real-Time User Mobility Pattern Modeling and Similarity Measurement for Mobile Social Networks


    Contributors:
    Ding, Feng (author) / Wang, Jian (author) / Zhang, Naitong (author) / Li, Wenfeng (author) / Zhao, Kanglian (author)


    Publication date :

    2016-05-01


    Size :

    384453 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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