4 citations · 8 across the 6 of their papers we have counts for
6 papers
TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
Ayeen Poostforoushan, Liane Vogel, Carsten Binnig
Tabular data is the dominant structured-data modality, and learning table representations has become a core research direction. Table-level embeddings in particular underpin a wide…
The Stretto Execution Engine for Semantic Data Systems
Gabriele Sanmartino, Matthias Urban, Paolo Papotti +1
Semantic data systems enable querying over both structured and unstructured data using LLMs, but a central challenge is navigating the performance-accuracy trade-off when selecting…
Unveiling Challenges for LLMs in Enterprise Data Engineering
Jan-Micha Bodensohn, Ulf Brackmann, Liane Vogel +2
Large Language Models (LLMs) promise to automate data engineering on tabular data, offering enterprises a valuable opportunity to cut the high costs of manual data handling. But th…
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…
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 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…