Lineage-based Probabilistic Event Stream Processing
Lineage-based Probabilistic Event Stream Processing
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DOI：
10.1109/mdmw.2008.12
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发表时间：
2008-04
期刊：
影响因子：
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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
在本文中，我们提出了一种查询语言，以支持复合事件流匹配的概率查询。该语言允许用户表达kleene封闭模式，以在物理世界中进行复杂的事件检测。我们还提出了一个工作框架，用于查询概率事件流。我们的方法首先通过使用新的数据结构来检测概率数据流的序列模式，AIG可以通过基于NFA的方法处理一组活动状态的记录集。检测活性状态后，我们的方法将计算其谱系上每个检测到的序列模式的概率。也就是说，查询处理和置信度计算是解耦的。通过谱系的好处，可以直接计算出输出事件的概率，而无需考虑查询计划。我们对我们的方法进行了绩效评估，与幼稚的方法相比，我们被称为可能的世界方法。结果清楚地表明了我们方法的有效性。尽管我们的方法显示出可扩展的吞吐量，但幼稚的方法会迅速降低其性能。实验是用窗口大小，事件类型的数量和替代方案进行的。
In this paper, we propose a query language to support probabilistic queries for composite event stream matching. The language allows users to express Kleene closure patterns for complex event detection in physical world. We also propose a working framework for query processing over probabilistic event streams. Our method first detects sequence patterns over probabilistic data streams by using a new data structure, AIG which handles a record sets of active states with a NFA-based approach. After detecting active states, our method then computes the probability of each detected sequence pattern on its lineage. That is, query processing and confidence computation are decoupled. By the benefit of lineage, the probability of an output event can be directly calculated without considering the query plan. We conduct a performance evaluation of our method comparing with naive one which is called possible worlds approach. The result clearly shows the effectiveness of our approach. While our approach shows scalable throughput, naive approach degrades its performance rapidly. The experiments are conducted with the window size, the number of event types and the number of alternatives.