PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation
Summary: PECJ enables SWJ on out-of-order streams by using unobserved data to boost accuracy and cut latency via variational posterior inference. Analytic and ML-based methods reduce error propagation and deliver high SWJ performance on a multi-thread benchmark. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Xianzhi Zeng (Singapore Institute of Technology)
- 2. Shuhao Zhang (Nanyang Technological University)
- 3. Hongbin Zhong (4Paradigm Inc.)
- 4. Hao Zhang (4Paradigm Inc.)
- 5. Mian Lu (4Paradigm Inc.)
- 6. Zhao Zheng (4Paradigm Inc.)
- 7. Yuqiang Chen (4Paradigm Inc.)
BibTeX Citation
@inproceedings{zeng_sigmod24,
title = {{PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation}},
author = {Zeng, Xianzhi and Zhang, Shuhao and Zhong, Hongbin and Zhang, Hao and Lu, Mian and Zheng, Zhao and Chen, Yuqiang},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639268},
url = {https://dl.acm.org/doi/10.1145/3639268},
year = {2024}
}
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