Magellan

• DOI: 10.14778/3007263.3007314
• Publication Date: 2016-11-01T13:47:47Z
• Authors: Pradap Konda; Sanjib Das; Paul Suganthan G. C.; AnHai Doan; Adel Ardalan; Jeffrey R. Ballard; Han Li; Fatemah Panahi; Haojun Zhang; Jeff Naughton; Shishir Prasad; Ganesh Krishnan; Rohit Deep; Vijay Raghavendra
• Keywords: 0301 basic medicine; 03 medical and health sciences; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Entity matching (EM) has been a long-standing challenge in data management. Most current EM works, however, 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 present Magellan, a new kind of EM systems that addresses these limitations. Magellan is novel in four important aspects. (1) It provides a how-to guide that tells users what to do in each EM scenario, step by step. (2) It provides tools to help users do these steps; the tools seek to cover the entire EM pipeline, not just matching and blocking as current EM systems do. (3) Tools are built on top of the data science stacks in Python, allowing Magellan to borrow a rich set of capabilities in data cleaning, IE, visualization, learning, etc. (4) Magellan provide a powerful scripting environment to facilitate interactive experimentation and allow users to quickly write code to "patch" the system. We have extensively evaluated Magellan with 44 students and users at various organizations. In this paper we propose demonstration scenarios that show the promise of the Magellan approach.