Spatiotemporal event sequences (STESs) are the ordered series of event types whose instances frequently follow each other in time and are located close-by. An STES is a spatiotemporal frequent pattern type, which is discovered from moving region objects whose polygon-based locations continiously evolve over time. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. The quality of the discovered sequences is of great importance to the domain experts who use these algorithms. We introduce a novel algorithm to find the most relevant STESs without threshold values. We tested the relevance and performance of our threshold-free algorithm with a case study on solar event metadata, and compared the results with the previous STES mining algorithms.

时空事件序列 (STES) 是事件类型的有序序列，其实例在时间上经常相互跟随并位于附近。STES 是一种时空频繁模式类型，它是从基于多边形的位置随时间不断演变的移动区域对象中发现的。以前关于 STES 挖掘的研究需要发现的重要性和流行阈值，这通常是领域专家不知道的。发现序列的质量对于使用这些算法的领域专家来说非常重要。我们引入了一种新的算法来找到最相关的 STES，没有阈值。我们通过对太阳事件元数据的案例研究测试了我们的无阈值算法的相关性和性能，并将结果与​​之前的 STES 挖掘算法进行了比较。