发现事件流中的一般偏序

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摘要翻译：
频繁事件发现是一种流行的事件流模式发现框架。插曲是节点的偏序集，每个节点都与事件类型相关联。当相关的部分顺序是总的（串行的）和琐碎的（并行的）时，存在有效的（和独立的）集发现算法。在本文中，我们提出了发现具有一般偏序的频繁剧集的有效算法。这些算法可以很容易地专门化，以发现串行或并行事件。同时，该算法具有足够的灵活性，可以专门用于在某些感兴趣的偏序子类空间中进行挖掘。我们指出在频繁偏序挖掘中存在一个固有的组合爆炸，最重要的是，频率本身并不是兴趣度的充分度量。针对一般偏序事件提出了一种新的兴趣度测度，并基于该测度提出了一种筛选无兴趣偏序的发现方法。仿真结果验证了算法的有效性。
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英文标题：
《Discovering general partial orders in event streams》
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作者：
Avinash Achar, Srivatsan Laxman, Raajay Viswanathan and P. S. Sastry
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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.
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PDF链接：
https://arxiv.org/pdf/0902.1227

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关键词：Presentation Intelligence Applications interesting Discovering episodes streams 子类 事件 兴趣