13 citations · 28 across the 10 of their papers we have counts for
10 papers · 1 filter
Efficient Learned Query Execution over Text and Tables [Technical Report]
Matthias Urban, Carsten Binnig
In this paper, we present ELEET, a novel execution engine that allows one to seamlessly query and process text as a first-class citizen along with tables. To enable such a seamless…
CardBench: A Benchmark for Learned Cardinality Estimation in Relational Databases
Yannis Chronis, Yawen Wang, Yu Gan +4
Cardinality estimation is crucial for enabling high query performance in relational databases. Recently learned cardinality estimation models have been proposed to improve accuracy…
SPARE: A Single-Pass Neural Model for Relational Databases
Benjamin Hilprecht, Kristian Kersting, Carsten Binnig
While there has been extensive work on deep neural networks for images and text, deep learning for relational databases (RDBs) is still a rather unexplored field. One direction tha…
CAESURA: Language Models as Multi-Modal Query Planners
Matthias Urban, Carsten Binnig
Traditional query planners translate SQL queries into query plans to be executed over relational data. However, it is impossible to query other data modalities, such as images, tex…
Towards Foundation Models for Relational Databases [Vision Paper]
Liane Vogel, Benjamin Hilprecht, Carsten Binnig
Tabular representation learning has recently gained a lot of attention. However, existing approaches only learn a representation from a single table, and thus ignore the potential…
Towards Multi-Modal DBMSs for Seamless Querying of Texts and Tables
Matthias Urban, Carsten Binnig
In this paper, we propose Multi-Modal Databases (MMDBs), which is a new class of database systems that can seamlessly query text and tables using SQL. To enable seamless querying o…