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• Learned Index Structures for Dynamic and Multi-Dimensional Data

Learned Index Structures for Dynamic and Multi-Dimensional Data

• See more Northwest Database Society talks here: https://db.cs.washington.edu/nwds/nwds.html
• Speaker: Jialin Ding

Abstract

Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with learned models. On read-only workloads, learned indexes can outperform state-of-the-art “traditional” indexes in both index size and lookup performance. However, past works on learned indexes perform poorly on workloads over changing or multi-dimensional data. In this talk, I will introduce some recent work that addresses these limitations of learned indexes. First, I will present ALEX, a fully dynamic learned index structure that simultaneously provides efficient support for point lookups, short range queries, inserts, updates, deletes, and bulk loading. I will then give a brief overview of Flood and Tsunami, learned multi-dimensional indexes that handle OLAP-style workloads that involve scanning and filtering over multi-dimensional tables. I will conclude by describing some directions for future work on learned indexes and how they fit into instance-optimized database systems.

Bio

I am a CS PhD in the MIT Data Systems Group, where I am advised by Prof. Tim Kraska. My research focuses on applying machine learning to database systems. I also collaborate with Umar Farooq Minhas and the Database Group at Microsoft Research on learned data structures. Prior to MIT, I was an undergraduate at Stanford University, where I worked on data-intensive systems with Prof. Peter Bailis as part of Stanford DAWN. My CV.