Creating probabilistic databases from duplicated data

Creating probabilistic databases from duplicated data A major source of uncertainty in databases is the presence of duplicate items, i.e., records that refer to the same real-world entity. However, accurate deduplication is a difficult task and imperfect data cleaning may result in loss of valuable information. A reasonable alternative approach is to keep duplicates when the correct cleaning strategy is not certain, and utilize an efficient probabilistic query-answering technique to return query results along with probabilities of each answer being correct. In this paper, we present a flexible modular framework for scalably creating a probabilistic database out of a dirty relation of duplicated data and overview the challenges raised in utilizing this framework for large relations of string data. We study the problem of associating probabilities with duplicates that are detected using state-of-the-art scalable approximate join methods. We argue that standard thresholding techniques are not sufficiently robust for this task, and propose new clustering algorithms suitable for inferring duplicates and their associated probabilities. We show that the inferred probabilities accurately reflect the error in duplicate records. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Creating probabilistic databases from duplicated data

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Publisher
Springer-Verlag
Copyright
Copyright © 2009 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-009-0161-2
Publisher site
See Article on Publisher Site

Abstract

A major source of uncertainty in databases is the presence of duplicate items, i.e., records that refer to the same real-world entity. However, accurate deduplication is a difficult task and imperfect data cleaning may result in loss of valuable information. A reasonable alternative approach is to keep duplicates when the correct cleaning strategy is not certain, and utilize an efficient probabilistic query-answering technique to return query results along with probabilities of each answer being correct. In this paper, we present a flexible modular framework for scalably creating a probabilistic database out of a dirty relation of duplicated data and overview the challenges raised in utilizing this framework for large relations of string data. We study the problem of associating probabilities with duplicates that are detected using state-of-the-art scalable approximate join methods. We argue that standard thresholding techniques are not sufficiently robust for this task, and propose new clustering algorithms suitable for inferring duplicates and their associated probabilities. We show that the inferred probabilities accurately reflect the error in duplicate records.

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2009

References

  • Swoosh: a generic approach to entity resolution
    Benjelloun, O.; Garcia-Molina, H.; Menestrina, D.; Su, Q.; Whang, S.E.; Widom, J.

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