DROPS

Document

Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)

Authors: Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, and Frank Hutter

Published in: Dagstuhl Reports, Volume 15, Issue 4 (2025)

Abstract

The growing volume and importance of structured data have sparked increasing interest in Table Representation Learning (TRL), an emerging field that leverages neural models to learn abstract, general-purpose representations for tabular data to support a wide range of downstream tasks such as tabular prediction, table question answering, tabular data cleaning, and many more. This seminar gathered the different communities (ML, NLP, IR, DB) who work on this topic to discuss the challenges & long-term vision of this field. From the organizers: Carsten Binnig, Julian Eisenschlos, Madelon Hulsebos, Frank Hutter.

Cite as

Carsten Binnig, Julian Martin Eisenschlos, Madelon Hulsebos, and Frank Hutter. Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182). In Dagstuhl Reports, Volume 15, Issue 4, pp. 126-138, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@Article{binnig_et_al:DagRep.15.4.126,
  author =	{Binnig, Carsten and Eisenschlos, Julian Martin and Hulsebos, Madelon and Hutter, Frank},
  title =	{{Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)}},
  pages =	{126--138},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2025},
  volume =	{15},
  number =	{4},
  editor =	{Binnig, Carsten and Eisenschlos, Julian Martin and Hulsebos, Madelon and Hutter, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.4.126},
  URN =		{urn:nbn:de:0030-drops-252531},
  doi =		{10.4230/DagRep.15.4.126},
  annote =	{Keywords: applications of table representation learning, benchmarks and datasets for table representation learning, pre-trained (language) models for tables and databases, representation and generative learning for data management and analysis}
}

Document

DOI: 10.4230/DagRep.14.4.54

Hardware Support for Cloud Database Systems in the Post-Moore’s Law Era (Dagstuhl Seminar 24162)

Authors: David F. Bacon, Carsten Binnig, David Patterson, and Margo Seltzer

Published in: Dagstuhl Reports, Volume 14, Issue 4 (2024)

Abstract

The end of scaling from Moore’s and Dennard’s laws has greatly slowed improvements in CPU speed, RAM capacity, and disk/flash capacity. Meanwhile, cloud database systems, which are the backbone for many large-scale services and applications in the cloud, are continuing to grow exponentially. For example, most of Google’s products that run on the Spanner database have more than a billion users and are continuously growing. Moreover, the growth in data also shows no signs of slowing down, with further orders-of-magnitude increases likely, due to autonomous vehicles, the internet-of-things, and human-driven data creation. Meanwhile, machine learning creates an appetite for data that also needs to be preprocessed using scalable cloud database systems. As a result, cloud database systems are facing a fundamental scalability wall on how to further support this exponential growth given the stagnation in hardware. While database research has a long tradition of investigating how modern hardware can be leveraged to improve overall system performance - which is also shown by the series of past Dagstuhl Seminars - a more holistic view is required to address the imminent exponential scalability challenge that databases will be facing. However, applying hardware accelerators in a database needs a careful design. In fact, so far, no commercial system has applied hardware accelerators at scale. Unlike other hyper-scale applications such as machine learning training and video processing where accelerators such as GPUs and TPUs circumvent this problem, workloads in cloud database systems are typically not compute-bound and thus benefit less or not at all from such existing accelerators. This Dagstuhl Seminar thus aimed to bring together leading researchers and practitioners from database systems, hardware architecture, and storage systems to rethink, from the ground up, how to co-design database systems and compute/storage hardware. By uniting experts across these disciplines, the seminar sought to identify the architectural changes and system designs that could enable the order-of-magnitude improvements required for the next generation of applications.

Cite as

David F. Bacon, Carsten Binnig, David Patterson, and Margo Seltzer. Hardware Support for Cloud Database Systems in the Post-Moore’s Law Era (Dagstuhl Seminar 24162). In Dagstuhl Reports, Volume 14, Issue 4, pp. 54-84, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@Article{bacon_et_al:DagRep.14.4.54,
  author =	{Bacon, David F. and Binnig, Carsten and Patterson, David and Seltzer, Margo},
  title =	{{Hardware Support for Cloud Database Systems in the Post-Moore’s Law Era (Dagstuhl Seminar 24162)}},
  pages =	{54--84},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{14},
  number =	{4},
  editor =	{Bacon, David F. and Binnig, Carsten and Patterson, David and Seltzer, Margo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.4.54},
  URN =		{urn:nbn:de:0030-drops-213521},
  doi =		{10.4230/DagRep.14.4.54},
  annote =	{Keywords: Databases, Modern Hardware, Cloud}
}

Search Results

Documents authored by Binnig, Carsten

Challenges and Opportunities of Table Representation Learning (Dagstuhl Seminar 25182)

Abstract

Cite as

Hardware Support for Cloud Database Systems in the Post-Moore’s Law Era (Dagstuhl Seminar 24162)

Abstract

Cite as

Thanks for your feedback!

Could not send message