RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
Paper • 2602.12606 • Published
name string | domain string | description string | num_tables int64 | num_rows int64 | num_cols int64 | num_tasks int64 | tasks_binary_classification int64 | tasks_regression int64 | tasks_multiclass_classification int64 | tasks_multilabel_classification int64 | tasks_link_prediction int64 | start_timestamp string | val_timestamp string | test_timestamp string | size_gb float64 | license string | source_url string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dbinfer-amazon | E-commerce (reviews) | Amazon from the 4DBInfer benchmark: a large product-review dataset linking users, products and reviews, used for rating prediction and user purchase/churn prediction. | 3 | 16,073,957 | 14 | 3 | 1 | 1 | 0 | 0 | 1 | 1996-06-25 00:00:00 | 2015-10-03 00:00:00 | 2015-12-30 00:00:00 | 5.7198 | No explicit license (McAuley Lab; citation requested) | https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/ |
dbinfer-avs | Retail (Acquire Valued Shoppers) | Acquire Valued Shoppers (AVS) from the 4DBInfer benchmark: a retail dataset of customer transaction histories and promotional offers, used to predict shopper behavior such as offer repeat purchases. | 8 | 350,196,856 | 27 | 1 | 1 | 0 | 0 | 0 | 0 | 2012-03-02 00:00:00 | 2013-04-24 00:00:00 | 2013-04-30 00:00:00 | 3.2896 | Kaggle competition rules: competition use only, no redistribution | https://www.kaggle.com/competitions/acquire-valued-shoppers-challenge/rules |
dbinfer-diginetica | E-commerce (sessions) | Diginetica from the 4DBInfer benchmark: an e-commerce dataset of user browsing and purchasing sessions over a product catalog (CIKM Cup 2016), used for click-through-rate and purchase prediction. | 12 | 97,258,572 | 32 | 2 | 1 | 0 | 0 | 0 | 1 | 2016-01-01 01:08:12.072000 | 2016-05-26 00:00:00 | 2016-05-31 00:00:00 | 0.2868 | No explicit license (CIKM Cup 2016 competition data) | https://competitions.codalab.org/competitions/11161 |
dbinfer-outbrain-small | Content recommendation | Outbrain (small) from the 4DBInfer benchmark: a content-recommendation dataset of document page views and promoted-content displays/clicks, used for click-through-rate prediction. | 9 | 4,213,483 | 34 | 1 | 1 | 0 | 0 | 0 | 0 | 1996-06-14 00:00:00 | 2016-06-24 00:00:00 | 2016-06-25 00:00:00 | 0.0501 | Kaggle competition rules: any purpose, commercial or non-commercial; redistribution not expressly granted | https://www.kaggle.com/competitions/outbrain-click-prediction/rules |
dbinfer-retailrocket | E-commerce (behaviour) | RetailRocket from the 4DBInfer benchmark: an e-commerce dataset of visitor events (views, add-to-cart, transactions) over an item catalog, used to predict conversion (whether a viewed item is later purchased). | 7 | 24,885,583 | 18 | 1 | 1 | 0 | 0 | 0 | 0 | 2015-05-03 03:00:04.384000 | 2015-09-16 00:00:00 | 2015-09-17 00:00:00 | 0.4256 | CC BY-NC-SA 4.0 | https://www.kaggle.com/datasets/retailrocket/ecommerce-dataset |
dbinfer-seznam | Digital advertising | Seznam from the 4DBInfer benchmark: a digital-advertising dataset from the Seznam.cz search engine, containing client prepaid-account charges and transactions, used to predict which service an account transacts on. | 4 | 2,688,678 | 14 | 2 | 0 | 0 | 2 | 0 | 0 | 2012-08-01 00:00:00 | 2015-04-01 00:00:00 | 2015-07-01 00:00:00 | 0.0211 | Research use (CTU Prague Relational Learning Repository); individual DBs inherit varied upstream licenses | https://relational.fel.cvut.cz/dataset/Seznam |
dbinfer-stackexchange | Online community (Q&A) | StackExchange from the 4DBInfer benchmark: the Cross Validated (stats.stackexchange.com) community-Q&A dataset of users, posts, votes and badges, used to predict user churn and post upvotes. | 9 | 6,140,680 | 48 | 2 | 2 | 0 | 0 | 0 | 0 | 2009-02-02 00:00:00 | 2020-05-10 00:00:00 | 2021-05-18 00:00:00 | 1.0021 | CC BY-SA 4.0 | https://archive.org/details/stackexchange |
dbinfer
The seven datasets of the 4DBInfer benchmark (data version 20240304) in RelBench format, built from the original archives. Labels are the source's own (kind: external). Where a task label is also a database column, the task declares remove_columns and get_db drops it. dbinfer-outbrain-small has almost no referential integrity in the source itself; see its card.
| dataset | tasks |
|---|---|
dbinfer-amazon |
churn, purchase, rating |
dbinfer-avs |
repeater |
dbinfer-diginetica |
ctr, purchase |
dbinfer-outbrain-small |
ctr |
dbinfer-retailrocket |
cvr |
dbinfer-seznam |
charge, prepay |
dbinfer-stackexchange |
churn, upvote |
<dataset>/
manifest.yaml # tables, primary keys, foreign-key graph, val/test timestamps
schema.svg # ER diagram
db/*.parquet # relational tables
tasks/<task>/manifest.yaml # task spec
tasks/<task>/{train,val,test}.parquet
import relbench
ds = relbench.load_dataset("stanford-star/dbinfer/dbinfer-diginetica")
task = relbench.load_task("stanford-star/dbinfer/dbinfer-diginetica", "ctr")
Revisions before the rebuild from the original archives had broken foreign keys, timestamps and targets; results computed against them are not comparable.
License
Each database keeps the license of its upstream source, listed in the license and source_url columns of STATS/databases.parquet. Our contributions (the conversion to this format, schemas, task definitions, splits and statistics) are CDLA-Permissive-2.0.
Citation
@inproceedings{wang2024fourdbinfer,
title = {{4DBInfer}: A {4D} Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational Databases},
author = {Wang, Minjie and Gan, Quan and Wipf, David and Cai, Zhenkun and Li, Ning and Tang, Jianheng and Zhang, Yanlin and Zhang, Zizhao and Mao, Zunyao and Song, Yakun and Wang, Yanbo and Li, Jiahang and Zhang, Han and Yang, Guang and Qin, Xiao and Lei, Chuan and Zhang, Muhan and Zhang, Weinan and Faloutsos, Christos and Zhang, Zheng},
booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
year = {2024}
}
@inproceedings{relbenchv2,
title={RelBench v2: A Large-Scale Benchmark and Repository for Relational Data},
author={Gu, Justin and Ranjan, Rishabh and Kanatsoulis, Charilaos and Tang, Haiming and Jurkovic, Martin and Hudovernik, Valter and Znidar, Mark and Chaturvedi, Pranshu and Shroff, Parth and Li, Fengyu and Leskovec, Jure},
booktitle={3rd Workshop on Navigating and Addressing Data Problems for Foundation Models (DATA-FM) at ICLR 2026},
year={2026},
note={arXiv:2602.12606 [cs.LG]},
url={https://arxiv.org/abs/2602.12606}
}
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