- 12/13/2024: 🎊 Provided data 🎉
- 10/29/2024: Preliminary repository created
This repository will contain the data and the code for our paper SALT: Sales Autocompletion Linked Business Tables Dataset to be presented at NeurIPS'24 Table Representation Workshop.
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.
Example of loading the tables with pandas. Unless already installed, install it with:
pip install pandasimport pandas as pd
# load the table data from the parquet files
salesdocuments = pd.read_parquet("data/I_SalesDocument.parquet")
salesdocument_items = pd.read_parquet("data/I_SalesDocumentItem.parquet")
customers = pd.read_parquet("data/I_Customer.parquet")
addresses = pd.read_parquet("data/I_AddrOrgNamePostalAddress.parquet")
# show the first elements
salesdocuments.head()
Table Schema of SALT Dataset
Example Input Mask of a Salesorder App using SAP S4/HANA
N/A
No known issues
If you use this code in your research or want to refer to our work, please cite:
@inproceedings{
klein2024salt,
title={{SALT}: Sales Autocompletion Linked Business Tables Dataset},
author={Tassilo Klein and Clemens Biehl and Margarida Costa and Andre Sres and Jonas Kolk and Johannes Hoffart},
booktitle={NeurIPS 2024 Third Table Representation Learning Workshop},
year={2024},
url={https://openreview.net/forum?id=UZbELpkWIr}
}
- Integration into RelBench, Feb'25
- Release dataset
Create an issue in this repository if you find a bug or have questions about the content.
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If you wish to contribute code, offer fixes or improvements, please send a pull request. Due to legal reasons, contributors will be asked to accept a DCO when they create the first pull request to this project. This happens in an automated fashion during the submission process. SAP uses the standard DCO text of the Linux Foundation.
Copyright (c) 2024 SAP SE or an SAP affiliate company. All rights reserved. This project is licensed under the CC-BY-NC-SA Software License, version 4.0 except as noted otherwise in the LICENSE file.
