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An Overview of End-to-End Entity Resolution for Big Data

Authors:

Vassilis Christophides,

Vasilis Efthymiou,

Themis Palpanas,

George Papadakis,

Kostas StefanidisAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 6

Article No.: 127, Pages 1 - 42

https://doi.org/10.1145/3418896

Published: 06 December 2020 Publication History

Abstract

One of the most critical tasks for improving data quality and increasing the reliability of data analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to the same real-world entity. Despite several decades of research, ER remains a challenging problem. In this survey, we highlight the novel aspects of resolving Big Data entities when we should satisfy more than one of the Big Data characteristics simultaneously (i.e., Volume and Velocity with Variety). We present the basic concepts, processing steps, and execution strategies that have been proposed by database, semantic Web, and machine learning communities in order to cope with the loose structuredness, extreme diversity, high speed, and large scale of entity descriptions used by real-world applications. We provide an end-to-end view of ER workflows for Big Data, critically review the pros and cons of existing methods, and conclude with the main open research directions.

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Index Terms

An Overview of End-to-End Entity Resolution for Big Data
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Information systems
  1. Data management systems
    1. Information integration
      1. Entity resolution

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 6

Invited Tutorial and Regular Papers

November 2021

803 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3441629

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Published: 06 December 2020

Accepted: 01 August 2020

Revised: 01 July 2020

Received: 01 April 2020

Published in CSUR Volume 53, Issue 6

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https://doi.org/10.26599/BDMA.2023.9020040
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