Systems | Information | Learning | Optimization

Entity Matching Meets Data Science: A Progress Report from the Magellan Project

Entity matching (EM) finds data instances that refer to the same real-world entity. In 2015, we started the Magellan project at UW-Madison 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 novel kind of EM systems, which is an ecosystem of interoperable tools. These tools often exploit machine learning, user interaction, and big data scaling techniques.
This talk provides a progress report on the past 3.5 years of Magellan, focusing on the system aspects and on how ideas from the field of data science have been adapted to the EM context. We begin by arguing 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, EM tools for power users and lay users. These tools have been successfully used in 22 EM tasks at 13 companies and domain science groups, and have been pushed into production for many customers. We report on the lessons learned, and outline a new envisioned Magellan ecosystem, which consists of not just on-premise Python tools, but also interoperable microservices deployed, executed, and scaled out on the cloud, using tools such as Dockers and Kubernetes.
February 6 @ 12:30
12:30 pm (1h)

Discovery Building, Orchard View Room

AnHai Doan

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