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Proceedings ArticleDOI

Robust and efficient fuzzy match for online data cleaning

TLDR
A new similarity function is proposed which overcomes limitations of commonly used similarity functions, and an efficient fuzzy match algorithm is developed which can effectively clean an incoming tuple if it fails to match exactly with any tuple in the reference relation.
Abstract
To ensure high data quality, data warehouses must validate and cleanse incoming data tuples from external sources. In many situations, clean tuples must match acceptable tuples in reference tables. For example, product name and description fields in a sales record from a distributor must match the pre-recorded name and description fields in a product reference relation.A significant challenge in such a scenario is to implement an efficient and accurate fuzzy match operation that can effectively clean an incoming tuple if it fails to match exactly with any tuple in the reference relation. In this paper, we propose a new similarity function which overcomes limitations of commonly used similarity functions, and develop an efficient fuzzy match algorithm. We demonstrate the effectiveness of our techniques by evaluating them on real datasets.

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Citations
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Journal ArticleDOI

Duplicate Record Detection: A Survey

TL;DR: This paper presents an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database and covers similarity metrics that are commonly used to detect similar field entries.
Journal ArticleDOI

Duplicate Record Detection: A Survey

TL;DR: This paper presents an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database and covers similarity metrics that are commonly used to detect similar field entries.
Journal ArticleDOI

Collective entity resolution in relational data

TL;DR: In this article, a relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities is proposed, which improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively.
Proceedings ArticleDOI

A Primitive Operator for Similarity Joins in Data Cleaning

TL;DR: This paper proposes a new primitive operator which can be used as a foundation to implement similarity joins according to a variety of popular string similarity functions, and notions of similarity which go beyond textual similarity.
Journal ArticleDOI

Information Extraction

TL;DR: A taxonomy of the field is created along various dimensions derived from the nature of the extraction task, the techniques used for extraction, the variety of input resources exploited, and the type of output produced to survey techniques for optimizing the various steps in an information extraction pipeline.
References
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Proceedings ArticleDOI

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Proceedings ArticleDOI

On the resemblance and containment of documents

Andrei Z. Broder
- 11 Jun 1997 - 
TL;DR: The basic idea is to reduce these issues to set intersection problems that can be easily evaluated by a process of random sampling that could be done independently for each document.