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Entity Resolution with Evolving Rules by Steven Euijong Whang; Hector Garcia-Molina is a Computer Science article available to read on EtoBox.

What is Entity Resolution with Evolving Rules about?

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 naïve 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 naïve approach.

Who reads Entity Resolution with Evolving Rules?

Author

Steven Euijong Whang; Hector Garcia-Molina

Publisher

ACM

Published

2010

Language

EN

Field

Computer Science (Physical Sciences)