计算机科学 ›› 2019, Vol. 46 ›› Issue (6): 55-63.doi: 10.11896/j.issn.1002-137X.2019.06.007
李志国1, 钟将2, 钟璐蔓1
LI Zhi-guo1, ZHONG Jiang2, ZHONG Lu-man1
摘要: 随着数据量变得不断庞大,将不同业务系统数据融合在一起挖掘潜在价值变得越来越有意义。复杂事件处理技术就是将业务数据抽象为事件序列,通过复杂事件描述方法将有潜在价值的复合数据描述为特定的事件匹配结构。复杂事件检测引擎从大量事件流中检测出满足匹配结构的事件序列,最终输出数据融合结果。但传统复杂事件描述只适用于输入事件流为单一原子事件类型,且谓词约束为简单的属性值比较或聚合操作,事件间为简单的时序约束。这使得传统检测方法无法满足诸如医学、金融等对时间要求比较精确、事件谓词约束要求更加丰富的应用领域。因此,设计了一种能够支持多元事件输入的基于TCN的量化时序约束表示模型和基于时段特征约束的谓词约束表示模型,并且提出了并行化的复杂事件检测算法(PARALLEL-TCSEQ-DETECTION检测算法),使得复杂事件检测方法更加高效。对2045支股票2亿条记录的分析结果表明了提出的复杂事件处理技术的可行性与高效性。
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