地 點：金沙官方体育 320 會議室
報告人：趙卓越 博士 美國猶他大學（The University of Utah）
報告題目：Approximate Query Processing via Sampling
Join operator is one of the most important one in relational algebra one. Yet, it is often very time-consuming to compute in full on a large database. On the other hand, it is often the case that a user
only needs a fairly accurate approximate query result for an aggregation query instead of an exact answer.
Towards that, we have investigated how we can efficiently retrieve samples directly from the join results
without computing them in full and how to utilize them to answer approximate queries. In this talk,
I will present two algorithms that apply to different application scenarioses, namely the Wander Join
algorithm which produces non-uniform but independent samples for approximate SPJA queries,
and a general random sampling framework that produces uniform and independent samples for other
types of complex analytical tasks. Experimental evaluation in real database systems show the algorithms
outperforms the state-of-the-art by orders of magnitude on the TPC-H benchmark dataset.
Zhuoyue Zhao is a fourthyear Ph.D. student at University of Utah and his advisor is Prof. Feifei Li.
He is interested in query processing and query optimization in OLAP databases and his main focus
is on approximate query processing and sampling from complex queries. Zhuoyue received the
best paper award in SIGMOD'16 and received the Google PhD Fellowship in 2019.
He received B.S.E degree from Shanghai Jiao Tong University.