Type: Research Highlight

Title: Magellan: Toward Building Entity Matching Management Systems

Pradap Konda, Sanjib Das, Paul Suganthan G.C., Philip Martinkus, AnHai Doan, Adel Ardalan, Jeffrey R. Ballard, Yash Govind, Han Li, Fatemah Panahi, Haojun Zhang, Jeff Naughton, Shishir Prasad, Ganesh Krishnan, Rohit Deep, Vijay Raghavendra

Available in: PDF

Entity matching (EM) has been a long-standing challenge in data management. Most current EM works focus only on developing matching algorithms. We argue that far more efforts should be devoted to building EM systems. We discuss the limitations of current EM systems, then describe Magellan, a new kind of EM system. Magellan is novel in four important aspects. (1) It provides how-to guides that tell users what to do in each EM scenario, step by step. (2) It provides tools to help users execute these steps; the tools seek to cover the entire EM pipeline, not just blocking and matching as current EM systems do. (3) Tools are built into the Python open-source data science ecosystem, allowing Magellan to borrow a rich set of capabilities in data cleaning, IE, visualization, learning, etc. (4) Magellan provides a powerful scripting environment to facilitate interactive experimentation and quick \patching” of the system.We describe research challenges and present extensive experiments that show the promise of the Magellan approach.

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