This paper considers the quickest search problem to identify anomalies among large numbers of data streams. These streams can model, for example, disjoint regions monitored by a mobile robot. A particular challenge is a version of the problem in which the experimenter must suffer a cost each time the data stream being sampled changes, such as the time the robot must spend moving between regions. In this paper, we propose an algorithm which accounts for switching costs by varying a confidence threshold that governs when the algorithm switches to a new data stream. Our main contributions are easily computable approximations for both the optimal value of this threshold and the optimal value of the parameter that determines when a stream must be re-sampled. Further, we empirically show (i) a uniform improvement for switching costs of interest and (ii) roughly equivalent performance for small switching costs when comparing to the closest available algorithm.

翻译：本文考虑在大量数据流中快速识别异常的最优搜索问题。这些数据流可模拟例如由移动机器人监控的多个不相邻区域。该问题的一个特殊挑战在于：每次切换被采样的数据流时，实验者需承担切换成本（如机器人移动至不同区域所消耗的时间）。本文提出一种通过调整控制算法切换数据流的置信阈值来计入切换成本的算法。我们的主要贡献包括：给出该阈值的最优值以及决定何时需对数据流重新采样的参数最优值的简易可计算近似解。此外，实证结果表明：（i）对于关注的切换成本，本算法实现了一致性改进；（ii）当切换成本较小时，本算法与现有最优算法性能大致相当。