Accuratecomprehension of users’ behavior is paramount for understanding the dynamics of several systems, such as e-commerce platforms, social networks, and mobile computing. To this end, several strategies have been proposed to obtain data sets based on the capture of usage information, which can then serve for user analytics. A popular strategy consists of taking periodic snapshots of online users, a practical instance of the coupon collector’s problem tailored to users monitoring in networked systems. Due to system-specific limitations, however, users may fail to appear in some snapshots, although online. To bridge this gap, we present a methodology to correct ill-collected snapshots and build more accurate data sets. In summary, we formally model user snapshotting as an instance of the coupon collector’s problem, estimate the probability that some users are missing in a given snapshot following a Bernoulli process, and correct those snapshots should the probability exceed a given threshold.

准确理解用户的行为对于理解几个系统的动态至关重要，例如电子商务平台，社交网络和移动计算。为此，已经提出了几种策略来基于使用信息的捕获来获取数据集，然后可以将其用于用户分析。一种流行的策略是对在线用户进行定期快照，这是针对在网络系统中监控用户的优惠券收集器问题的实际实例。但是，由于特定于系统的限制，尽管在线，用户仍可能无法出现在某些快照中。为了弥合这一差距，我们提出了一种方法来纠正不良收集的快照并建立更准确的数据集。总而言之，我们正式将用户快照建模为优惠券收集者的问题的一个实例，