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Accurate and efficient record linkage is an open challenge of particularrelevance to Australian Government Agencies, who recognise that so-calledwicked social problems are best tackled by forming partnerships founded onlarge-scale data fusion. Names and addresses are the most common attributes onwhich data from different government agencies can be linked. In this paper, wefocus on the problem of address linking. Linkage is particularly problematicwhen the data has significant quality issues. The most common approach fordealing with quality issues is to standardise raw data prior to linking. If amistake is made in standardisation, however, it is usually impossible torecover from it to perform linkage correctly. This paper proposes a novelalgorithm for address linking that is particularly practical for linking largedisparate sets of addresses, being highly scalable, robust to data qualityissues and simple to implement. It obviates the need for labour intensive andproblematic address standardisation. We demonstrate the efficacy of thealgorithm by matching two large address datasets from two government agencieswith good accuracy and computational efficiency.

Exploiting Redundancy, Recurrence and Parallelism: How to Link Millions of Addresses with Ten Lines of Code in Ten Minutes