With the rapid development of Internet of Things (IoT) and mobile technologies, the service offerings available in the IoT and mobile environments are increasing dramatically. How to provide intelligent and personalized services for users becomes a challenging issue. Several context aware service recommendation approaches have been reported to leverage roles to represent common knowledge within user communities, based on which services can be recommended for users. Prior studies on context aware role mining mainly focus on mining roles from a fixed data set of user behavior patterns, while most of them neglect the dynamic change of the input data. The frequent change of the user data will result in the change of extracted roles, and how to efficiently update extracted roles according to change of the input user data remains a challenging issue. In this paper, towards this issue, the authors introduce a novel role updating approach in context aware role mining. In the apporach, several algorithms are presented towards various scenarios such as new users and new contexts are removed from and added into the input data. Experiments show that compared with existing solutions, the proposed algorithms can guarantee the completeness of updating results while keeping good updating efficiency.
A Scalable Role Mining Approach for Large Organizations
