Recording available to view at: https://mediaspace.illinois.edu/media/t/1_rvnzpmpu
Abstract:
Despite advances in computing power, the cost of large-scale analytics and machine learning remains daunting to small and large enterprises alike. This has created a pressing demand for reducing infrastructure costs and query latencies. To meet these goals, researchers and data engineers are looking for new approaches that can best exploit the various opportunities available from modern computing environments while minimally disrupting their existing heterogeneous data stacks. To tackle this, my research investigates intelligent solutions that can optimize large-scale data operations. In this talk, I will focus on two specific directions. First, I will present VerdictDB, a system that enables quality-guaranteed, statistical tradeoffs without changing backend infrastructure. Second, I will introduce a learning-based solution that can constantly learn from their past executions and become “smarter” over time without any user intervention. I will conclude by briefly discussing other promising directions with emerging workloads beyond SQL.
Bio:
Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. At UIUC, he is part of Data and Information Systems (DAIS) Research Lab. Also, Yongjoo is the CTO of Keebo, Inc., a start-up company he co-founded based on his Ph.D. research. Yongjoo's research interest is in building intelligent data-intensive systems using statistical and Artificial Intelligence techniques. Yongjoo obtained a Ph.D. in Computer Science and Engineering from the University of Michigan, Ann Arbor in 2017. His dissertation received the 2018 SIGMOD Jim Gray Dissertation runner-up award.
Part of the Illinois Computer Science Speakers Series. Faculty Host: Chengxiang Zhai.