---

license: cc-by-4.0 tags:

• relbench
• relational-deep-learning pretty_name: RelBench dbinfer datasets size_categories:
• 100M<n<1B configs:
• config_name: databases data_files:
  • split: eval path: STATS/databases.parquet
• config_name: tasks data_files:
  • split: eval path: STATS/tasks.parquet---

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.

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}
}