To process a query in database systems, the query optimizer selects the most efficient plan among the possible execution plans of the query. Without query optimization, database systems would be highly inefficient. Since the cost of a plan is estimated with the result size of each operator in the plan, the accurate cardinality estimation of subqueries is essential to produce an optimal execution plan of a query. Thus, there have been extensive works using histograms, wavelet synopses and locality sensitive hashing techniques for cardinality estimation of queries. Since deep learning models can reflect the underlying patterns and correlations of data well, deep learning models are recently investigated and shown to outperform the traditional methods for cardinality estimation of queries. In my talk, I will present an overview of the traditional as well as deep learning methods developed for cardinality estimation of queries in database systems.
Kyuseok Shim is a Professor at Department of Electrical and Computer Engineering in Seoul National University, Korea. Before that, he was an Assistant Professor at Computer Science Department in KAIST (Korea), a member of technical staff at Bell Laboratories (Murray Hill) and a member of Quest Data Mining project at IBM Almaden Research Center. He is currently an Editor-In-Chief of the VLDB Journal and was previously an Associate Editor for the IEEE TKDE, VLDB as well as PVLDB journals. He also served as a Program Co-chair for PAKDD 2003, WWW 2014, ICDE 2015, APWeb 2016, BigComp 2019 and ICDM 2019 conferences and have been serving on Program Committees of the leading database as well as data mining conferences including SIGMOD, SIGKDD, ICDE, ICDM, EDBT, VLDB, WWW and CIKM. He became an ACM fellow and an IEEE fellow for the contributions to scalable data mining and query processing. He was previously a member of the VLDB Endowment Board of Trustees and is currently a steering committee member of PAKDD as well as DASFAA conferences. He served as the president of the Korean Information Scientist and Engineers (KIISE) in 2022 and became a member of National Academy of Engineering of Korea in 2023. He has been working in the area of data mining, machine learning, privacy preservation, query processing, query optimization, data warehousing, semi-structured data (XML), stream data and histograms.