Candidate Seminar by Dr. Xin Xia (University of British Columbia)
Speaker: Dr. Xin Xia
University of British Columbia
Title: Bridging the Gaps between Data Miners and Domain Experts
Date: Tuesday August 29, 2017
Today, data miners often apply or extend data mining techniques to solve problems across many domains (e.g., social media, health informatics, and software systems); while domain experts leverage their own domain knowledge to solve their own problems. Data miners often apply their automated techniques to solve a wide range of problems across different domains with limited knowledge of the domain; while domain experts often have limited knowledge of automated techniques when solving their domain-specific problems.
My research tries to bridge the gap between both types of experts (i.e., Data miners and Domain Experts). In this talk, I will focus on the software engineering domain and I will give an overview of several challenges facing data miner and domain experts as they make use of automated techniques, in particular: (1) although we have many easy-to-use data mining tools, many domain experts have limited knowledge of these tools, which often causes research bias; (2) strong performance of techniques is not sufficient, instead a deeper understanding of the domain is essential; (3) results should be presented in a domain-centric context . I will present examples from my research to explain what these challenges are, why do they appear, and my efforts to avoid them.
Xin Xia received his PhD degree in computer science from the College of Computer Science and Technology, Zhejiang University, China in 2014. He is currently a post-doctoral research fellow in the software practices lab at the University of British Columbia, Canada. Prior to joining UBC, he was a research assistant professor at Zhejiang University in China for two years. Xin has published 79 papers to date, and many papers appeared in premier software engineering venues, such as TSE, EMSE, ASE, ISSTA, ICSME, and MSR. To help developers and testers improve their productivity, his current research focuses on mining and analyzing rich data in software repositories to uncover interesting and actionable information.
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