OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
Summary: OEBench: benchmark of 55 real-world relational streams revealing open-environment issues (drift, missing values, anomalies, evolving features) overlooked by synthetic evaluations. Evaluation shows incremental learners often fail—more data doesn't guarantee accuracy—and existing methods fall short; datasets/code released. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yiqun Diao (National University of Singapore)
- 2. Yutong Yang (National University of Singapore)
- 3. Qinbin Li (University of California Berkeley)
- 4. Bingsheng He (National University of Singapore)
- 5. Mian Lu (4Paradigm Inc.)
BibTeX Citation
@article{diao_vldb24,
title = {{OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams}},
author = {Diao, Yiqun and Yang, Yutong and Li, Qinbin and He, Bingsheng and Lu, Mian},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {6},
pages = {1283--1296},
doi = {10.14778/3648160.3648170},
url = {https://doi.org/10.14778/3648160.3648170},
year = {2024}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,390 | ARM-Net: Adaptive Relation Modeling Network for Structured Data | 2021 | SIGMOD | 6.7290926e-05 |
| 4,814 | METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection | 2024 | VLDB | 6.4955135e-05 |
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