SALT: Sales Autocompletion Linked Business Tables Dataset

TL;DR

This paper introduces SALT, a dataset of linked enterprise tables from an ERP system, aimed at advancing research in table representation learning for real-world business applications.

Contribution

It provides the first curated dataset of extensive linked enterprise tables to facilitate research in structured data modeling and business intelligence.

Findings

Dataset enables new research in linked table modeling.

Supports development of models for enterprise data analysis.

Enhances applicability of AI in business contexts.

Abstract

Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.