Towards Multi-Modal DBMSs for Seamless Querying of Texts and Tables
- URL: http://arxiv.org/abs/2304.13559v2
- Date: Fri, 28 Apr 2023 14:55:48 GMT
- Title: Towards Multi-Modal DBMSs for Seamless Querying of Texts and Tables
- Authors: Matthias Urban and Carsten Binnig
- Abstract summary: We propose to extend databases with so-called multi-modal relational operators (MMOps)
MMOps allow text collections to be treated as tables without the need to manually transform the data.
Our MMDB prototype can not only outperform state-of-the-art approaches such as text-to-table in terms of accuracy and performance.
- Score: 14.249508312922334
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 of textual data using SQL in an MMDB, we propose to
extend relational databases with so-called multi-modal operators (MMOps) which
are based on the advances of recent large language models such as GPT-3. The
main idea of MMOps is that they allow text collections to be treated as tables
without the need to manually transform the data. As we show in our evaluation,
our MMDB prototype can not only outperform state-of-the-art approaches such as
text-to-table in terms of accuracy and performance but it also requires
significantly less training data to fine-tune the model for an unseen text
collection.
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