70 citations · 110 across the 4 of their papers we have counts for
8 papers · 1 filter
From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management
Immanuel Trummer
Large language models have recently advanced the state of the art on many natural language processing benchmarks. The newest generation of models can be applied to a variety of tas…
CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex
Immanuel Trummer
CodexDB is an SQL processing engine whose internals can be customized via natural language instructions. CodexDB is based on OpenAI's GPT-3 Codex model which translates text into c…
UDO: Universal Database Optimization using Reinforcement Learning
Junxiong Wang, Immanuel Trummer, Debabrota Basu
UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants…
Optimally Summarizing Data by Small Fact Sets for Concise Answers to Voice Queries
Immanuel Trummer, Connor Anderson
Our goal is to find combinations of facts that optimally summarize data sets. We consider this problem in the context of voice query interfaces for simple, exploratory data analysi…
Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verification
Georgios Karagiannis, Mohammed Saeed, Paolo Papotti +1
Organizations such as the International Energy Agency (IEA) spend significant amounts of time and money to manually fact check text documents summarizing data. The goal of the Scru…
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning
Immanuel Trummer, Junxiong Wang, Deepak Maram +3
SkinnerDB is designed from the ground up for reliable join ordering. It maintains no data statistics and uses no cost or cardinality models. Instead, it uses reinforcement learning…