发现事件流中的一般偏序
Discovering general partial orders in event streams
摘要：
频繁事件发现是一种流行的事件流模式发现框架。一个事件是一个部分有序的节点集，每个节点与一个事件类型相关联。有效的（和单独的）算法存在的情节发现时，相关的偏序是总（串行情节）和平凡（并行情节）。在本文中，我们提出了有效的算法发现频繁情节一般偏序。这些算法可以很容易地专门用于发现串行或并行事件。此外，算法是足够灵活的，专门用于挖掘空间中的某些有趣的偏序子类。我们指出，在频繁偏序挖掘中存在固有的组合爆炸，最重要的是，频率本身并不足以衡量兴趣度。我们提出了一个新的兴趣度一般偏序情节和发现方法，基于此措施，过滤掉不感兴趣的偏序。仿真实验证明了算法的有效性。
英文摘要：
Frequent episode discovery is a popular framework for pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Efficient (and separate) algorithms exist for episode discovery when the associated partial order is total (serial episode) and trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with general partial orders. These algorithms can be easily specialized to discover serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that there is an inherent combinatorial explosion in frequent partial order mining and most importantly, frequency alone is not a sufficient measure of interestingness. We propose a new interestingness measure for general partial order episodes and a discovery method based on this measure, for filtering out uninteresting partial orders. Simulations demonstrate the effectiveness of our algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
