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Mining frequent arrangements of temporal intervals

机译：挖掘时间间隔的频繁安排

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The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Three efficient methods to find frequent arrangements of temporal intervals are described; the first two are tree-based and use breadth and depth first search to mine the set of frequent arrangements, whereas the third one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.
机译：研究发现时间间隔频繁安排的问题。假定数据库由事件序列组成，其中事件在时间间隔内发生。目的是挖掘频繁出现在数据库中的事件间隔的时间安排。这项工作的动机是观察到实际上，大多数事件不是瞬时的，而是在一段时间内发生的，并且不同事件可能同时发生。因此，有许多实际应用需要挖掘间隔之间的这种时间相关性，包括对来自美国手语的注释数据以及网络和生物数据的语言分析。描述了找到时间间隔的频繁安排的三种有效方法。前两个是基于树的，并使用广度和深度优先搜索来挖掘频繁安排的集合，而第三个是基于前缀的。以上方法应用了有效的修剪技术，该技术包括一组约束，这些约束将用户控制的焦点添加到挖掘过程中。此外，基于提取的模式，采用了一种用于挖掘关联规则的标准方法，该方法应用了不同的兴趣度度量来评估发现的模式和规则的重要性。对所提出算法的性能进行了评估，并与其他方法（在美国手语注释和网络数据）和大型综合数据集上进行了比较。

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