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RSkNN: kNN Search on Road Networks by Incorporating Social Influence
  • RSkNN: kNN Search on Road Networks by Incorporating Social Influence

RSkNN: kNN Search on Road Networks by Incorporating Social Influence

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RSkNN: kNN Search on Road Networks by Incorporating Social Influence

Finding k nearest objects to a query user q on Gr called as kNN search on a road network Gr is the extensively used method. The existing system neglects the fact that the q’s social information can play an important role in this k NN query.RSkNN: kNN Search on Road Networks by Incorporating Social Influence Many real-world applications, such as location-based social networking services, require such a query.

The proposed system performs a study on new problem: k NN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-art Independent Cascade (IC) model in social network is applied to define social influence.RSkNN: kNN Search on Road Networks by Incorporating Social Influence

One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, a three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based), and hybrid indexing algorithms is used.

For tackling the hard problem of computing social influence in the RN-based algorithm a filtering-and-verification framework is employed. To speed up the query in the SN-based algorithm, we embed social cuts into the index. To obtain query answers efficiently in the hybrid algorithm, an index, summarizing the road and social networks is used. Finally, we use real road and social network data to empirically verify the efficiency of the proposed solutions.

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