Efficient probabilistic event stream processing with lineage and Kleene-plus
Efficient probabilistic event stream processing with lineage and Kleene-plus
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DOI：
10.1504/ijcnds.2009.026554
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发表时间：
2009-06
影响因子：
1.3
通讯作者：
Zhitao Shen;H. Kawashima;H. Kitagawa
中科院分区：
文献类型：
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作者：
Zhitao Shen;H. Kawashima;H. Kitagawa
本文提出了一个工作框架和查询语言，以支持概率查询的概率事件流上的复合事件检测。该语言允许用户表达Kleene闭包模式，用于物理世界中的复杂事件检测。我们的处理方法首先检测序列模式的概率数据流使用AIG，一种新的数据结构，它处理活动状态的非确定性有限自动机（NFA）。然后，我们的方法根据其谱系计算每个检测到的序列模式的概率。通过沿袭的好处，可以直接计算输出事件的概率，而无需考虑查询计划。可以选择最佳方案。最后，我们进行了性能评估，我们的方法，并与原始和优化的查询计划的结果进行了比较。实验清楚地表明，我们的建议优于直接查询计划。
This paper proposes a working framework and a query language to support probabilistic queries for composite event detection over probabilistic event streams. The language allows users to express Kleene closure patterns for complex event detection in the physical world. Our processing method first detects sequence patterns over probabilistic data streams using AIG, a new data structure, which handles active states with a nondeterministic finite automaton (NFA). Our method then computes the probability of each detected sequence pattern based on its lineage. Through the benefit of lineage, the probability of an output event can be directly calculated without taking into account the query plan. An optimised plan can be selected. Finally, we conducted a performance evaluation of our method and compared the results with the original and optimised query plan. The experiment clearly showed that our proposal outperforms straight-forward query plans.