FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data
Summary: FlashP enables real-time forecasting over high-dimensional relational time series by replacing bottleneck aggregations with sampled approximations. Its GSW sampling provides aggregation-error bounds, compact multi-measure samples, and principled sample sizing for forecasting accuracy. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Shuyuan Yan (Alibaba)
- 2. Bolin Ding (Alibaba)
- 3. Wei Guo (Alibaba)
- 4. Jingren Zhou (Alibaba)
- 5. Zhewei Wei (Renmin University of China)
- 6. Xiaowei Jiang (Alibaba)
- 7. Sheng Xu (Alibaba)
BibTeX Citation
@article{yan_vldb21,
title = {{FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data}},
author = {Yan, Shuyuan and Ding, Bolin and Guo, Wei and Zhou, Jingren and Wei, Zhewei and Jiang, Xiaowei and Xu, Sheng},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {5},
pages = {721--729},
doi = {10.14778/3446095.3446096},
url = {https://doi.org/10.14778/3446095.3446096},
year = {2021}
}
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