A Novel Record Linkage Interface That Incorporates Group Structure to Rapidly Collect Richer Labels Linking historical data longitudinally allows researchers to better characterize topics like population mobility, the impact of local  national events, and generational changes. The ideal linking process would involve subject matter experts with detailed information about each record, including any relationships to other records, however, this indepth process is expensive and often infeasible. Record linkage is the process of identifying and labeling records corresponding to unique entities. These statistical models largely rely on pairwise comparisons, underutilizing information about group structure and historical knowledge. Moreover, model performance can be limited by using labels of unknown certainty or origin. In record linkage, we are rarely given information about the number of labelers, how often they agreed, or the labeling process itself. Understanding how and why records are linked together for the dual purposes of gaining insights into the human decisionmaking process and improving record linkage models is an exciting, high impact area of research. We present an interactive labeling interface for use at the initial stages of the (potentially crowdsourced) record linkage process. The interface captures labeled records while tracking the labeler actions. The interface allows labelers to view and interact with the records at both the individual and group level, thereby providing nested labels. We simultaneously receive information about the label certainty and the labeleru0027s decisionmaking process via repeated label instances and clickstreams. We demonstrate the utility of this interface on the recently released, unlabeled 1901 and 1911 Ireland Census records and discuss the benefits of richer labels.

Efficient Sequential and Parallel Algorithms for Incremental Record Linkage Using Complete Linkage Clustering In the biomedical domain, the record linkage is considered as a crucial problem. When the number of records is very large, existing algorithms for record linkage take too much time. Often, we have to link a small set of new records with a large set of old records. This can be done by putting together the old and new records and performing a linkage on all the records. Clearly, this will call for an enormous amount of time. An alternative is to develop algorithms that perform linkage in an incremental manner. We refer to any such algorithm as an Incremental Record Linkage (IRL) algorithm. In this paper we present an efficient IRL algorithm. In addition to taking large amounts of time, existing algorithms might also suffer from a chaining problem and hence introduce some errors in linking. As has been observed in the literature, this chaining problem can be solved by performing clustering under complete linkage. The IRL algorithm we present in this paper employs complete linkage and is called as Incremental Record Linking Algorithm using Complete Linkage xe2x80x9cIRLACLxe2x80x9d. We propose sequential and parallel versions of this algorithm. IRLACL can handle any number of datasets. In contrast, many of the existing algorithms can only link two datasets at a time. Our algorithm outperforms previous algorithms and offer stateoftheart solutions to the IRL problem as well. Our algorithms have been tested on millions of records on synthetic and real datasets and outperform the bestknown RLACL algorithm when the number of new records is up to around 20% of the total number of old records and achieve a very nearly linear speedup in parallel.