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
- 2. Yutong Yang
- 3. Qinbin Li
- 4. Bingsheng He
- 5. Mian Lu
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,148 | ARM-Net: Adaptive Relation Modeling Network for Structured Data | 2021 | SIGMOD | 7.4751269e-05 |
| 4,762 | METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection | 2024 | VLDB | 5.9395463e-05 |
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