Many emerging applications are based on finding interesting subsequences from sequence data. Finding “prominent streaks,” a set of the longest contiguous subsequences with values all above (or below) a certain threshold, from sequence data is one of that kind that receives much attention. Motivated from real applications, we observe that prominent streaks alone are not insightful enough but require the discovery of something we coined as “historic moments” as companions. In this article, we present an algorithm to efficiently compute historic moments from sequence data. The algorithm is incremental and space optimal , meaning that when facing new data arrival, it is able to efficiently refresh the results by keeping minimal information. Case studies show that historic moments can significantly improve the insights offered by prominent streaks alone. Furthermore, experiments show that our algorithm can outperform the baseline in both time and space.

中文翻译：

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序列数据中的历史时刻发现

许多新兴应用基于从序列数据中寻找有趣的子序列。从序列数据中寻找“显着条纹”，即一组最长的连续子序列，其值都高于（或低于）某个阈值，是一种备受关注的类型。受实际应用的启发，我们观察到仅突出的条纹还不够有洞察力，但需要发现我们称之为“历史时刻”的东西作为伴侣。在本文中，我们提出了一种算法，可以有效地从序列数据中计算历史时刻。该算法是增加的和空间最优，这意味着当面对新的数据到来时，它能够通过保持最少的信息有效地刷新结果。案例研究表明，历史性时刻可以显着改善仅由突出条纹提供的洞察力。此外，实验表明我们的算法在时间和空间上都可以优于基线。