4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Paper • 2404.18209 • Published
name stringclasses 7
values | domain stringclasses 6
values | description stringclasses 7
values | num_tables int64 3 9 | num_rows int64 2.17M 350M | num_tasks int64 1 3 | tasks_binary_classification int64 0 2 | tasks_multiclass_classification int64 0 2 | size_gb float64 0.03 5.91 | val_timestamp stringdate 2013-07-30 00:00:00 2023-09-05 00:00:00 | test_timestamp stringdate 2013-07-31 00:00:00 2023-09-06 00:00:00 | license stringclasses 1
value | source_url stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | 3 | 1 | 2 | 5.9107 | 2016-01-03 00:00:00 | 2016-01-04 00:00:00 | see 4DBInfer / original sources | https://github.com/awslabs/multi-table-benchmark |
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. | 3 | 349,815,883 | 1 | 1 | 0 | 5.5635 | 2013-07-30 00:00:00 | 2013-07-31 00:00:00 | see 4DBInfer / original sources | https://github.com/awslabs/multi-table-benchmark |
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. | 8 | 96,552,556 | 2 | 1 | 1 | 0.4584 | 2016-11-11 00:00:00 | 2016-11-12 00:00:00 | see 4DBInfer / original sources | https://github.com/awslabs/multi-table-benchmark |
dbinfer-outbrain-small | News / ad clicks | 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. | 8 | 2,170,445 | 1 | 1 | 0 | 0.0619 | 2016-09-03 00:00:00 | 2016-09-04 00:00:00 | see 4DBInfer / original sources | https://github.com/awslabs/multi-table-benchmark |
dbinfer-retailrocket | E-commerce (sessions) | 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). | 5 | 23,011,215 | 1 | 1 | 0 | 0.4897 | 2015-09-20 00:00:00 | 2015-09-21 00:00:00 | see 4DBInfer / original sources | https://github.com/awslabs/multi-table-benchmark |
dbinfer-seznam | Search / 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 account charging/prepayment behavior. | 4 | 2,688,678 | 2 | 0 | 2 | 0.0266 | 2015-10-03 00:00:00 | 2015-10-04 00:00:00 | see 4DBInfer / original sources | https://github.com/awslabs/multi-table-benchmark |
dbinfer-stackexchange | Online Q&A | StackExchange from the 4DBInfer benchmark: a community-Q&A dataset of users, posts, votes and badges, used to predict user churn and post upvotes. | 9 | 6,140,680 | 2 | 2 | 0 | 1.0385 | 2023-09-05 00:00:00 | 2023-09-06 00:00:00 | see 4DBInfer / original sources | https://github.com/awslabs/multi-table-benchmark |
RelBench dbinfer datasets
This repository hosts the dbinfer family of relational datasets in the RelBench 3.0
manifest format, one subdirectory per dataset. The datasets originate from the
4DBInfer benchmark (data version
20240304) and are exposed to RelBench via the dbinfer-relbench-adapter package. Their
labels are built externally and served as-is (every task has kind: external).
Each subdirectory is a self-describing RelBench dataset (manifest.yaml + plain db/*.parquet
tasks/<task>/); open itsschema.svgfor a zoomable entity-relationship diagram.
Datasets
| dataset | domain | tasks |
|---|---|---|
dbinfer-avs |
Acquire Valued Shoppers retail transactions | repeater |
dbinfer-diginetica |
E-commerce browsing/purchase sessions (CIKM Cup 2016) | ctr, purchase |
dbinfer-retailrocket |
E-commerce visitor events | cvr |
dbinfer-seznam |
Seznam.cz advertising account charges | charge, prepay |
dbinfer-amazon |
Amazon product reviews | rating, purchase, churn |
dbinfer-stackexchange |
StackExchange community Q&A | churn, upvote |
dbinfer-outbrain-small |
Outbrain content recommendation | ctr |
(Only datasets actually present as subdirectories are available; see each subdirectory's card for details.)
Loading
import relbench
ds = relbench.load_dataset("dbinfer-diginetica") # or any dataset above
task = relbench.load_task("dbinfer-diginetica", "ctr")
db = ds.get_db()
train = task.get_table("train")
See the RelBench CONTRIBUTING guide for the manifest layout.
Citation
These datasets are from the 4DBInfer benchmark. If you use them, please cite:
@article{dbinfer,
title={4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs},
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},
journal={arXiv preprint arXiv:2404.18209},
year={2024}
}
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