The Experts below are selected from a list of 559179 Experts worldwide ranked by ideXlab platform

Jacky Xi Zhang - One of the best experts on this subject based on the ideXlab platform.

  • location information disclosure in location based social network services
    Computers in Human Behavior, 2015
    Co-Authors: Nan Wang, Xiaoliang Shen, Jacky Xi Zhang
    Abstract:

    Hedonic benefits have stronger impacts on perceived benefits than utilitarian benefits.There is an interaction effect between perceived benefits and privacy risks.Males stress on utilitarian benefits while females stress on hedonic benefits.Males stress on perceived benefits while females stress on privacy risks. As the mechanisms underlying users' location information disclosure behavior under the context of location-based social network services (LBSNS) have been rarely investigated, this study builds a research model to examine the privacy calculus, benefit structure and gender differences. Specifically, hedonic benefits have stronger impacts on perceived benefits than utilitarian benefits and there is an interaction effect between perceived benefits and privacy risks. Further, utilitarian (hedonic) benefits are more influential for males (females) than for females (males) when formulating overall benefit perceptions, and perceived benefits (privacy risks) have stronger impacts on disclosure intention for males (females) than for females (males). Theoretical and practical implications are also discussed.

Liangyu Chen - One of the best experts on this subject based on the ideXlab platform.

Wenhao Yu - One of the best experts on this subject based on the ideXlab platform.

  • spatial co location pattern mining for location based services in road networks
    Expert Systems With Applications, 2016
    Co-Authors: Wenhao Yu
    Abstract:

    A new approach for mining spatial co-location patterns was presented.The neighborhood was refined using network distances rather than Euclidean ones.An efficient algorithm to build the neighborhood relationship graph was proposed.The performance of the proposed algorithm was explored. With the evolution of geographic information capture and the emergency of volunteered geographic information, it is getting more important to extract spatial knowledge automatically from large spatial datasets. Spatial co-location patterns represent the subsets of spatial features whose objects are often located in close geographic proximity. Such pattern is one of the most important concepts for geographic context awareness of location-based services (LBS). In the literature, most existing methods of co-location mining are used for events taking place in a homogeneous and isotropic space with distance expressed as Euclidean, while the physical movement in LBS is usually constrained by a road network. As a result, the interestingness value of co-location patterns involving network-constrained events cannot be accurately computed. In this paper, we propose a different method for co-location mining with network configurations of the geographical space considered. First, we define the network model with linear referencing and refine the neighborhood of traditional methods using network distances rather than Euclidean ones. Then, considering that the co-location mining in networks suffers from expensive spatial-join operation, we propose an efficient way to find all neighboring object pairs for generating clique instances. By comparison with the previous approaches based on Euclidean distance, this approach can be applied to accurately calculate the probability of occurrence of a spatial co-location on a network. Our experimental results from real and synthetic data sets show that the proposed approach is efficient and effective in identifying co-location patterns which actually rely on a network.

Nan Wang - One of the best experts on this subject based on the ideXlab platform.

  • location information disclosure in location based social network services
    Computers in Human Behavior, 2015
    Co-Authors: Nan Wang, Xiaoliang Shen, Jacky Xi Zhang
    Abstract:

    Hedonic benefits have stronger impacts on perceived benefits than utilitarian benefits.There is an interaction effect between perceived benefits and privacy risks.Males stress on utilitarian benefits while females stress on hedonic benefits.Males stress on perceived benefits while females stress on privacy risks. As the mechanisms underlying users' location information disclosure behavior under the context of location-based social network services (LBSNS) have been rarely investigated, this study builds a research model to examine the privacy calculus, benefit structure and gender differences. Specifically, hedonic benefits have stronger impacts on perceived benefits than utilitarian benefits and there is an interaction effect between perceived benefits and privacy risks. Further, utilitarian (hedonic) benefits are more influential for males (females) than for females (males) when formulating overall benefit perceptions, and perceived benefits (privacy risks) have stronger impacts on disclosure intention for males (females) than for females (males). Theoretical and practical implications are also discussed.

E M Eddy - One of the best experts on this subject based on the ideXlab platform.