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The Cornell Database Group is interested in all aspects of data analysis and database management. This includes projects at the intersection between database systems and other areas such as machine learning or natural language processing. For recent news, visit the Cornell Database Group Homepage or follow us on Twitter.
Recent Publications
2022
- VLDB 2022, PhD Workshop Building learned federated query optimizers. Victor Giannakouris, Immanuel Trummer.
- VLDB 2022 CodexDB: generating code for processing SQL queries using GPT-3 Codex. Immanuel Trummer.
- VLDB 2022 Black-box optimization of comparative data summaries via reinforcement learning. Immanuel Trummer.
- VLDB 2022 From BERT to GPT-3 Codex: harnessing the potential of very large language models for data management. Immanuel Trummer.
- VLDB 2022 UDO: universal database optimization using reinforcement learning. Junxiong Wang, Immanuel Trummer, Debabrota Basu.
- SIGMOD 2022 Demonstrating DB-BERT: a database tuning tool that “reads the manual”. Immanuel Trummer.
- AAAI 2022 Procrastinated tree search: black-box optimization with delayed, noisy, and multi-fidelity feedback. Junxiong Wang, Debabrota Basu, Immanuel Trummer.
- SIGMOD 2022 DB-BERT: a database tuning tool that “reads the manual”. Immanuel Trummer.
- CIDR 2022 Towards NLP-enhanced data profiling tools. (Abstract) Immanuel Trummer.
2021
- TODS 2021 “Best of SIGMOD” Edition SkinnerDB: regret-bounded query evaluation via reinforcement learning. Immanuel Trummer, Junxiong Wang, Ziyun Wei et al.
- VLDB 2021 The case for NLP-enhanced database tuning: towards tuning tools that read the manual. Immanuel Trummer.
- VLDB 2021 Robust voice querying with MUVE: optimally visualizing results of phonetically similar queries. Ziyun Wei, Immanuel Trummer, Connor Anderson.
- IEEE Data Engineering Bulletin WebChecker: towards an infrastructure for efficient misinformation detection at Web scale. Immanuel Trummer.
- SIGMOD Record 2021 Database tuning using natural language processing. Immanuel Trummer.
- SIGMOD 2021 Demonstrating UDO: a unified approach for optimizing transaction code, physical design, and system parameters via reinforcement learning. Junxiong Wang, Immanuel Trummer, Debabrota Basu.
- SIGMOD 2021 Demonstrating robust voice querying with MUVE: optimally visualizing results of phonetically similar queries. Ziyun Wei, Immanuel Trummer, Connor Anderson.
- ICDE 2021 Optimally summarizing data by small fact sets for concise answers to voice queries. Immanuel Trummer, Connor Anderson.