Using Bayesian Generative Models with Apache Spark to Solve Entity Resolution Problems at Scale
Summary
Entity resolution finds duplicate or near-identical profiles that represent the same real-world entity, a task where brute-force comparison on datasets over roughly 10 million records becomes infeasible. A Bayesian statistical sampling approach on Spark matches records at scale using KD-tree partitioning to distribute data across nodes, plus attribute similarity functions.
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