Article

Record linkage: similarity measures and algorithms

Authors:

Nick Koudas,

Sunita Sarawagi, and

Divesh SrivastavaAuthors Info & Claims

SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data

June 2006

Pages 802 - 803

https://doi.org/10.1145/1142473.1142599

Published: 27 June 2006 Publication History

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Abstract

This tutorial provides a comprehensive and cohesive overview of the key research results in the area of record linkage methodologies and algorithms for identifying approximate duplicate records, and available tools for this purpose. It encompasses techniques introduced in several communities including databases, information retrieval, statistics and machine learning. It aims to identify similarities and differences across the techniques as well as their merits and limitations.

References

[1]

C. Batini, T. Catarci, and M. Scannapieco. A survey of data quality issues in cooperative information systems. Pre-conference ER tutorial, 2004.

Google Scholar

[2]

T. Johnson and P. Dasu. Data quality and data cleaning: An overview. SIGMOD tutorial, 2003.

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[3]

N. Koudas and D. Srivastava. Approximate joins: concepts and techniques. VLDB tutorial, 2005.

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Cited By

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Yang LYe HLiu XMao YZhang J(2023)Authenticating q-Gram-Based Similarity Search Results for Outsourced String DatabasesMathematics10.3390/math1109212811:9(2128)Online publication date: 1-May-2023
https://doi.org/10.3390/math11092128
Manzano FAvalos D(2023)Análisis de calidad de los datos en las estadísticas públicas y privadas, ante la implementación del Big DataCiencias Administrativas10.24215/23143738e119(119)Online publication date: 14-Mar-2023
https://doi.org/10.24215/23143738e119
Shu YZhang JHuang GChi CHe J(2023)Entity alignment via graph neural networks: a component-level studyWorld Wide Web10.1007/s11280-023-01221-826:6(4069-4092)Online publication date: 29-Nov-2023
https://doi.org/10.1007/s11280-023-01221-8
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Index Terms

Record linkage: similarity measures and algorithms
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data

June 2006

830 pages

ISBN:1595934340

DOI:10.1145/1142473

General Chairs:
Clement Yu
University of Illinois at Chicago
,
Peter Scheuermann
Northwestern University
,
Program Chair:
Surajit Chaudhuri
Microsoft Research

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 27 June 2006

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SIGMOD/PODS06

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SIGMOD/PODS06: International Conference on Management of Data and Symposium on Principles Database and Systems

June 27 - 29, 2006

IL, Chicago, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Yang LYe HLiu XMao YZhang J(2023)Authenticating q-Gram-Based Similarity Search Results for Outsourced String DatabasesMathematics10.3390/math1109212811:9(2128)Online publication date: 1-May-2023
https://doi.org/10.3390/math11092128
Manzano FAvalos D(2023)Análisis de calidad de los datos en las estadísticas públicas y privadas, ante la implementación del Big DataCiencias Administrativas10.24215/23143738e119(119)Online publication date: 14-Mar-2023
https://doi.org/10.24215/23143738e119
Shu YZhang JHuang GChi CHe J(2023)Entity alignment via graph neural networks: a component-level studyWorld Wide Web10.1007/s11280-023-01221-826:6(4069-4092)Online publication date: 29-Nov-2023
https://doi.org/10.1007/s11280-023-01221-8
Zhao XZeng WTang JZhao XZeng WTang J(2023)Introduction to Entity AlignmentEntity Alignment10.1007/978-981-99-4250-3_1(3-13)Online publication date: 26-Oct-2023
https://doi.org/10.1007/978-981-99-4250-3_1
樊沁(2022)Entity Resolution Algorithm Based on Locality Sensitive Hash and Fuzzy JoinHans Journal of Data Mining10.12677/HJDM.2022.12302812:03(280-296)Online publication date: 2022
https://doi.org/10.12677/HJDM.2022.123028
Ilyas IRekatsinas TKonda VPound JQi XSoliman MIves ZBonifati AEl Abbadi A(2022)Saga: A Platform for Continuous Construction and Serving of Knowledge at ScaleProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526049(2259-2272)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526049
Ilyas IRekatsinas T(2022)Machine Learning and Data Cleaning: Which Serves the Other?Journal of Data and Information Quality10.1145/350671214:3(1-11)Online publication date: 21-Jul-2022
https://dl.acm.org/doi/10.1145/3506712
Ma HGuan SToomey CWu YSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Diversified Subgraph Query Generation with Group FairnessProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498525(686-694)Online publication date: 11-Feb-2022
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Ma HGuan SWang MChang YWu Y(2022)Subgraph Query Generation with Fairness and Diversity Constraints2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00278(3106-3118)Online publication date: May-2022
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Muneer INawab R(2022)Cross-Lingual Text Reuse Detection at sentence level for English–Urdu language pairComputer Speech and Language10.1016/j.csl.2022.10138175:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.csl.2022.101381
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A taxonomy of privacy-preserving record linkage techniques

Improving record linkage performance in the presence of missing linkage data

Scalable Privacy-Preserving Record Linkage for Multiple Databases

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A taxonomy of privacy-preserving record linkage techniques

Improving record linkage performance in the presence of missing linkage data

Scalable Privacy-Preserving Record Linkage for Multiple Databases

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