# An overview of end-to-end entity resolution for big data

DevFeed: [An overview of end-to-end entity resolution for big data](<https://devfeed.tech/articles/an-overview-of-end-to-end-entity-resolution-for-big-data-28595.md>)

Original publisher: [Read original article](<https://blog.acolyer.org/2020/12/14/entity-resolution/>)

Author: adriancolyer

Published: 2020-12-14T14:37:00Z

Content type: article

Language: en

Sources: [Adrian Colyer](<https://devfeed.tech/sources/adrian-colyer.md>)

Topics: [Entity resolution](<https://devfeed.tech/topics/entity-resolution.md>), [big-data](<https://devfeed.tech/topics/big-data.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [algorithms-and-data-structures](<https://devfeed.tech/tags/algorithms-and-data-structures.md>), [article](<https://devfeed.tech/tags/article.md>), [big-data](<https://devfeed.tech/tags/big-data.md>), [blocking](<https://devfeed.tech/tags/blocking.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [comparisons](<https://devfeed.tech/tags/comparisons.md>), [overview](<https://devfeed.tech/tags/overview.md>), [uncategorized](<https://devfeed.tech/tags/uncategorized.md>)

## AI overview

This article summarizes an ACM Computing Surveys survey on end-to-end entity resolution for big data. It explains the main pipeline stages: blocking to reduce comparisons, block processing to remove redundant and superfluous comparisons, matching entity-description pairs, and clustering matches into resolved entities. It also outlines classification dimensions including schema awareness, matching process, and batch or incremental processing.

## Source excerpt

An overview of end-to-end entity resolution for big data, Christophides et al., ACM Computing Surveys, Dec. 2020, Article No. 127 The ACM Computing Surveys are always a great way to get a quick orientation in a new subject area, and hot off the press is this survey on the entity resolution (aka record linking) problem. It's an ... Continue reading An overview of end-to-end entity resolution for big data