Intelligent state machine for social ad hoc data management and reuse

Recent advances in information technology have turned out World Wide Web to be the main platform for interactions where participants—users and corresponding events—are triggered. Although the participants vary in accordance with scenarios, a considerable size of data will be generated. This phenomenon indeed causes the complexity in information retrieval, management, and resuse, and meanwhile, turns down the value of this data. In this research, we attempt to achieve efficient management of user-generated data and its derivative contexts (i.e., social ad hoc data) for human supports. The correlations among data, contexts, and their hybridization are specifically concentrated. An intelligent state machine is proposed to outline the relations of data and contexts, and applied to further identify their usage scenarios. The performance and feasibility can be revealed by the experiments that were conducted on the data collected from open social networks (e.g., Facebook, Twitter, etc.) in the past few years with size around 500 users and 8,000,000 shared contents from them.