Dr. Wei Wang is a Professor in the School of Computer Science and Engineering, The University of New South Wales, Australia. His current research interests include Similarity Query Processing, Artificial Intelligence, Knowledge Graphs, and Security for AI Models. He has published over a hundred research papers, with many in premier database journals (TODS, VLDB J, and TKDE) and conferences (SIGMOD, VLDB, ICDE, WWW, IJCAI, AAAI, ACL). More can be found on his homepage at: http://www.cse.unsw.edu.au/~weiw/
Similarity query processing is an essential procedure in a wide range of applications. Recently, embedding and auto-encoding methods as well as pre-trained models have gained popularity. Besides reviewing exact and approximate methods such as cover tree, locality sensitive hashing, product quantization, and proximity graphs, in this tutorial, we also discuss the selectivity estimation problem and show how researchers are bringing in state-of-the-art ML techniques to address the problem. By highlighting the strong connections between DB and ML, we hope that this tutorial provides an impetus towards new ML for DB solutions and vice versa.