Record linkage is the task of combining records from multiple files which refer to overlapping sets of entities when there is no unique identifying field. In streaming record linkage, files arrive sequentially in time and estimates of links are updated after the arrival of each file. This problem arises in settings such as longitudinal surveys, electronic health records, and online events databases, among others. The challenge in streaming record linkage is to efficiently update parameter estimates as new data arrive. We approach the problem from a Bayesian perspective with estimates calculated from posterior samples of parameters and present methods for updating link estimates after the arrival of a new file that are faster than fitting a joint model with each new data file. In this paper, we generalize a two-file Bayesian Fellegi-Sunter model to the multi-file case and propose two methods to perform streaming updates. We examine the effect of prior distribution on the resulting linkage accuracy as well as the computational trade-offs between the methods when compared to a Gibbs sampler through simulated and real-world survey panel data. We achieve near-equivalent posterior inference at a small fraction of the compute time. Supplemental materials for this article are available online.

翻译：记录链接是将来自多个文件、指向重叠实体集合的记录进行合并的任务，当不存在唯一标识字段时尤为常见。在流式记录链接中，文件按时间顺序依次到达，链接估计值会在每个文件到达后更新。该问题出现在纵向调查、电子健康记录和在线事件数据库等场景中。流式记录链接的挑战在于如何在新数据到达时高效更新参数估计值。我们从贝叶斯视角出发，利用参数后验样本计算估计值，并提出了在新文件到达后更新链接估计值的方法，其速度优于对每个新数据文件拟合联合模型。本文将一个双文件贝叶斯Fellegi-Sunter模型推广至多文件情形，并提出了两种执行流式更新的方法。通过模拟数据和真实世界调查面板数据，我们研究了先验分布对链接准确性的影响，以及这些方法与吉布斯采样器相比在计算性能上的权衡。我们以极低的计算时间实现了近乎等价的后验推断。本文的补充材料可在线获取。