Service Hyperlink: Modeling and Reusing Partial Process Knowledge by Mining Event Dependencies among Sensor Data Services

In an IoT environment, process analysis becomes more difficult as a process usually spans over a set of autonomous and distributed sensors. This paper consummates our previous service hyperlink model, to encapsulate dependencies among events generated from services. To effectively discover service hyperlinks, we transform the service hyperlink discovery problem into a frequent sequence mining problem. Existing frequent sequence mining algorithms cannot be directly used because they do not take the temporal constraints in event dependencies into consideration. Based on the dataset from a real power plant as well as several synthetic datasets, we do lots of experiments to verify the effectiveness and efficiency of our algorithm.