Title: Fast Bayesian record linkage for streaming data contexts
Authors: Ian Taylor - Colorado State University (United States)
Andee Kaplan - Colorado State University (United States)
Brenda Betancourt - NORC at the University of Chicago (United States)
Andee Kaplan - Colorado State University (United States) [presenting]
Abstract: 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 the records. 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 a new data file arrives. We approach the problem from a Bayesian perspective with estimates in the form of posterior samples of parameters and present methods for updating link estimates after the arrival of a new file that is faster than fitting a joint model with each new data file. 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.