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Magellan: Toward Building Ecosystems of Entity Matching Solutions

By AnHai Doan, Pradap Konda, Paul Suganthan G. C., Yash Govind, Derek Paulsen, Kaushik Chandrasekhar, Philip Martinkus, Matthew Christie

Communications of the ACM, Vol. 63 No. 8, Pages 83-91

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Entity matching (EM) finds data instances that refer to the same real-world entity. In 2015, we started the Magellan project at UW-Madison, jointly with industrial partners, to build EM systems. Most current EM systems are stand-alone monoliths. In contrast, Magellan borrows ideas from the field of data science (DS), to build a new kind of EM systems, which is ecosystems of interoperable tools for multiple execution environments, such as on-premise, cloud, and mobile. This paper describes Magellan, focusing on the system aspects. We argue why EM can be viewed as a special class of DS problems and thus can benefit from system building ideas in DS. We discuss how these ideas have been adapted to build PyMatcher and CloudMatcher, sophisticated on-premise tools for power users and self-service cloud tools for lay users. These tools exploit techniques from the fields of machine learning, big data scaling, efficient user interaction, databases, and cloud systems. They have been successfully used in 13 companies and domain science groups, have been pushed into production for many customers, and are being commercialized. We discuss the lessons learned and explore applying the Magellan template to other tasks in data exploration, cleaning, and integration.

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1. Introduction

Entity matching (EM) finds data instances that refer to the same real-world entity, such as tuples (David Smith, UW-Madison) and (D. Smith, UWM). This problem, also known as entity resolution, record linkage, deduplication, data matching, et cetera, has been a long-standing challenge in the database, AI, KDD, and Web communities.2,6

As data-driven applications proliferate, EM will become even more important. For example, to analyze raw data for insights, we often integrate multiple raw data sets into a single unified one, before performing the analysis, and such integration often requires EM. To build a knowledge graph, we often start with a small graph and then expand it with new data sets, and such expansion requires EM. When managing a data lake, we often use EM to establish semantic linkages among the disparate data sets in the lake.


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