Incremental entity resolution on rules and data

Incremental entity resolution on rules and data 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 first address the problem of keeping the ER result up-to-date when the ER logic or data “evolve” 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 and data. We introduce algorithm properties that facilitate evolution, and we propose efficient rule and data evolution techniques for three ER models: match-based clustering (records are clustered based on Boolean matching information), distance-based clustering (records are clustered based on relative distances), and pairs ER (the pairs of matching records are identified). Using real datasets, we illustrate the cost of materializations and the potential gains of evolution over the naïve approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Incremental entity resolution on rules and data

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2013 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-013-0315-0
Publisher site
See Article on Publisher Site

Abstract

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 first address the problem of keeping the ER result up-to-date when the ER logic or data “evolve” 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 and data. We introduce algorithm properties that facilitate evolution, and we propose efficient rule and data evolution techniques for three ER models: match-based clustering (records are clustered based on Boolean matching information), distance-based clustering (records are clustered based on relative distances), and pairs ER (the pairs of matching records are identified). Using real datasets, we illustrate the cost of materializations and the potential gains of evolution over the naïve approach.

Journal

The VLDB JournalSpringer Journals

Published: Apr 17, 2013

References

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