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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)

Paper ID
6884
Venue
SIGMOD
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,159 | 23.44%
DOI
10.1145/3639268

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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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