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    Guo Chi, Fang Yuan, Liu Jingnan, Wan Yi. Social Awareness Computation Methods for Location Based Services[J]. Journal of Computer Research and Development, 2013, 50(12): 2531-2542.
    Citation: Guo Chi, Fang Yuan, Liu Jingnan, Wan Yi. Social Awareness Computation Methods for Location Based Services[J]. Journal of Computer Research and Development, 2013, 50(12): 2531-2542.

    Social Awareness Computation Methods for Location Based Services

    • Positioning technologies, especially outdoor positioning, have been extensively developed recently. There has been massive location and user data which can reflect abundant social information. With the information, location based services can be intelligent and personalized. Thus the technology for location based awareness computation is needed urgently. Social awareness computation in location based services is a kind of computational technology. This technology utilizes the mass position devices deployed in human social life space to achieve following goals: 1) Analyze and recognize the behaviors of social individuals; 2) Analyze the characteristics and laws of social community interaction; 3) Guide individual social behavior; and 4) Support community interaction, communication, and collaboration. Social awareness computation is the key of transferring location based service from simple positioning service to social computation pattern. This paper focuses on these methods, and thus addresses three issues: First of all, what is location based social awareness and its framework; Secondly, what is the relationship between social properties of locations and human behaviors; And at last, what are the common awareness and data mining methods in real analysis and system application, especially in big data analysis of locations. This paper gives details from four parts: social semantic awareness of locations, location based relationship awareness of users, mobility awareness of users, and location based social characteristic awareness of users. This paper systematically classifies and generalizes those methods from the two aspects: computation models and evaluation methods.
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