Modern information systems generate large volumes of data with anomalies that occur at unknown points in time and have to be detected quickly and reliably with low false alarm rates. The paper develops a general theory of quickest multistream detection in non-i.i.d. stochastic models when a change may occur in a set of multiple data streams. The first part of the paper focuses on the asymptotic quickest detection theory. Nearly optimal pointwise detection strategies that minimize the expected detection delay are proposed and analyzed when the false alarm rate is low. The general theory is illustrated in several examples. In the second part, we discuss challenging applications associated with the rapid detection of new COVID waves and the appearance of near-Earth space objects. Finally, we discuss certain open problems and future challenges.

翻译：现代信息系统生成大量数据，其中包含在未知时间点发生的异常，需要以低虚警率快速可靠地检测。本文针对非独立同分布随机模型中的最快多流检测问题，发展了通用理论。第一部分侧重于渐近最快检测理论，在低虚警率条件下提出并分析了能使期望检测延迟最小化的近最优逐点检测策略，并通过多个实例阐释了该通用理论。第二部分讨论了与快速检测新冠传播新浪潮及近地空间天体出现相关的挑战性应用。最后，探讨了若干开放问题与未来挑战。