挖掘时空事件数据中频繁闭合嵌入子有向无环图，提升模式发现效率。
Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data
- 用节点和边表示事件类型及时空先后关系，挖掘闭合子有向无环图模式
- 在相同参数下，算法效率显著优于SLEUTH和CSTPM方法
- 适用于交通、社交等复杂事件序列分析，适合模式发现研究者
我们提出一种新方法，通过发现频繁闭合嵌入子有向无环图（sub-DAGs）来挖掘时空事件数据中的模式。事件实例以事件类型标签的节点表示，边则刻画时空先后关系。我们正式定义了这类模式，并论证选择闭合子DAG作为紧凑且非冗余的重复交互模式表示。我们实现了DigDag算法用于挖掘此类模式，并在相同参数设置下，与基于SLEUTH的传播模式挖掘和基于CSTPM的级联时空模式挖掘方法进行了实验比较。结果表明，本方法在效率上具有显著优势。最后，我们对部分发现的模式进行了定性分析。
原文摘要 · Abstract (English)
We propose a novel approach to mine patterns in spatio-temporal event data based on discovering frequent closed embedded sub-Directed Acyclic Graphs (DAGs). In our method, event instances are represented as nodes labelled by event types, while edges capture spatio-temporal following relationships. We formally define the considered class of patterns and provide the rationale for focusing on closed sub-DAGs as compact and non-redundant representations of recurring interaction patterns. We implement the DigDag algorithm for mining such patterns and experimentally compare its efficiency with two related approaches: propagation pattern mining using the SLEUTH algorithm and Cascading Spatio-Temporal Pattern mining using the CSTPM algorithm. The experimental results demonstrate that our approach is substantially more efficient while operating under comparable parameter settings. Finally, we present a qualitative analysis of selected discovered patterns.
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