Entity Resolution with Evolving Rules

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Entity Resolution with Evolving Rules
Entity resolution (ER) identifies database records that refer to the same real world entity. In practice, ER is not a one-time process, but is constantly improved as the data, schema and application are better understood. We address the problem of keeping the ER result up-to-date when the ER logic “evolves” frequently. A na¨ıve approach that re-runs ER from scratch may not be tolerable for resolving large datasets. This paper investigates when and how we can instead exploit previous “materialized” ER results to save redundant work with evolved logic. We introduce algorithm properties that facilitate evolution, and we propose efficient rule evolution techniques for two clustering ER models: match-based clustering and distance-based clustering. Using real data sets, we illustrate the cost of materializations and the potential gains over the na¨ıve approach.
Steven Whang, Hector Garcia-Molina
Added 30 Jan 2011
Updated 30 Jan 2011
Type Journal
Year 2010
Authors Steven Whang, Hector Garcia-Molina
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