January 27, 2023
11:00am - 12:00pm

Columnar databases enable effective compression by improving entropy through attribute locality and provides opportunities for fast query execution directly on compressed data. In this talk I will briefly overview how compressed query execution works in columnar systems and discuss techniques developed by our group over the past several years. This includes a pattern-inferred attribute decomposition for improved string compression and query performance, a bounded float compression technique for fast filtering on limited precision numeric data, and partially ordered dictionary compression.

Aaron J. Elmore is an Associate Professor in the Department of Computer Science, and the College of the University of Chicago. Aaron was previously a Postdoctoral Associate at MIT working with Mike Stonebraker on elastic and multitenant database systems, and Sam Madden on the DataHub project. Aaron's thesis on Elasticity Primitives for Database-as-a-Service was completed at the University of California, Santa Barbara under the supervision of Divy Agrawal and Amr El Abbadi. His recent research interests focus on building data systems that address the growing data deluge. This includes building a resource-efficient database system (CrocodileDB), new compression techniques for databases (DenseStore and EdgeTSD), bringing git-like functionality to Databases (Datahub, Decibel, and OrpheusDB), and data discovery (DataSwamp and Relic). His previous projects included seminal work on polystores (BigDAWG), elastic databases (E-Store), and database migration (Squall and Zephyr). He is the recipient of the NSF Early CAREER award and is currently an associate editor for SIGMOD record, ICDE panel co-chair, and VLDB workshop co-chair.