Whang, Steven Euijong and Garcia-Molina, Hector Entity Resolution with Evolving Rules. Technical Report. Stanford InfoLab.
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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 naive 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 three ER models: join, match-based clustering, and distance-based clustering. Using real data sets, we illustrate the cost of materializations and the potential gains over the naive approach.
|Item Type:||Techreport (Technical Report)|
|Deposited By:||Steven Whang|
|Deposited On:||08 Mar 2010 08:24|
|Last Modified:||02 Jul 2010 10:43|
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