Differently from the general online social network (OSN), location based mobile social network (LMSN), which seamlessly integrates mobile computing and social computing technologies, has unique characteristics of temporal, spatial and social correlation. Recommending friends instantly based on current location of users in the real world has become increasingly popular in LMSN. However, the existing friend recommendation methods based on topological structures of a social network or non-topological information such as similar user profiles cannot well address the instant making friends in the real world. In this article, we analyze users' check-in behavior in a real LMSN site named Gowalla. According to this analysis, we present an approach of recommending friends instantly for LMSN users by considering the real-time physical location proximity, offline behavior similarity and friendship network information in the virtual community simultaneously. This approach effectively bridges the gap between the offline behavior of users in the real world and online friendship network information in the virtual community. Finally, we use the real user check-in dataset of Gowalla to verify the effectiveness of our approach.
A considerable number of business process modeling approaches are developed for end-users in recent years. The core issue of end-user oriented business process modeling is how to fill the gap between end-user friendly business process model and professional IT process model. In our previous work, a wizard based lightweight event-driven modeling method is designed for end-users to create their own personalized process. We observe that such a process created by end-users often cause execution time errors, which cannot be effectively detected by existing modeling languages. In this paper, we propose a formal model based on extended event-driven process chain (eEPC), which is named lightweight event-driven process chain (lightEPC). We give complete formalization of lightEPC and present two error patterns and corresponding detection algorithms to perform design-time model checking based on this model. These approaches effectively help endusers to create correct business process satisfying their personalized requirements.
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