Paper Title
Sequence Tree: A Data Structure For Improving Learning Sequence Constraint

Complex event processing (CEP) system is aimed at finding the rule-satisfied condition from plenty of event flows, generating a composite event, and then sending it to the interested components. Usually, rules used in the CEP System are defined by relevant experts. But for various reasons, it is a really difficult task to define perfect rules. This situation stimulates the works about CEP rules learning. Sequence between the primitive events in a CEP rule is indispensable part of CEP rules, and the most former works on learning sequence constraint are extracting the positive trace in the history record directly. Through lots of experiments we find out that different positive traces have exactly similar number of primitive events and their sequential relations under the same size of the window. Base on that we propose a new data structure—sequence tree to combine the same positive traces to avoid unnecessary computing for improving efficiency of the algorithm. In the end of the paper we design a set of experiments, and the experimental results validate the thought that using the sequence tree to combining the positive trace is feasibly and effectively. Index Items- complex event processing, rule learning, sequence constraint, combining positive trace