基于情节规则匹配的数据流预测

摘要

This paper proposes an algorithm called Predictor. This algorithm uses an automaton per matched episode rule with general form. With the aim of finding the latest minimal and non-overlapping occurrence of all antecedents, Predictor simultaneously tracks the state transition of each automaton by a single scanning of data stream, which can not only map the boundless streaming data into the finite state space but also ?avoid over-matching episode rules. In addition, the results of Predictor contain the occurring intervals and occurring probabilities of future episodes. Theoretical analysis and experimental evaluation demonstrate Predictor has higher prediction efficiency and prediction precision.%提出了一种数据流预测算法Predictor.该算法为每个待匹配的一般形式的情节规则分别使用了一个自动机,通过单遍扫描数据流来同时跟踪这些自动机的状态变迁,以搜索每个规则前件最近的最小且非重叠发生.这样不仅将无界的数据流映射到有限的状态空间,而且避免了对情节规则的过于匹配.另外,算法预测的结果是未来多个情节的发生区间和发生概率.理论分析和实验评估表明,Predictor具有较高的预测效率和预测精度.