Constructing the Sequential Event Graph for Event Prediction towards Cyber-Physical Systems

Abstract

One of the primary goals of cyber-physical system is to deeply integrate cyberspace and the physical world to realize intelligent interaction of the system. Event prediction technique is a powerful means to fulfill this goal. Recently, a novel knowledge graph, the event graph, is widely studied in the field of event analysis due to its excellent ability in event relationship modeling. Therefore, this paper proposes constructing the event graph to model the sequential event evolution in the physical world for event prediction. To this end, the sequential event graph construction method and related event prediction mechanism for CPSs are proposed. First, a flexible and universal paradigm is designed to assist in extracting event instances from the data generated by physical devices. Then, an automatic event graph construction method based on frequent episode mining is proposed. Finally, the related prediction mechanism is designed, including the identification of contextual information a nd the prediction of subsequent events. A case study on car usage illustrates the feasibility of our approach. The flexibility and support for complexity are demonstrated by a comparative discussion.