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Time Series Data Encoding for Efficient Storage: A Comparative Analysis in Apache IoTDB

Summary: Analyzes how scale, deltas, repetition, and monotonicity affect time-series encoding within Apache IoTDB’s storage constraints. Introduces a feature-controlled benchmark plus industrial datasets, yielding comparative quantitative guidance for encoder selection. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12899
Venue
VLDB
Year
2022
Pagerank
6.519484e-05
Overall Rank
4,762 | 67.33%
DOI
10.14778/3547305.3547319

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{xiao_vldb22,
        title = {{Time Series Data Encoding for Efficient Storage: A Comparative Analysis in Apache IoTDB}},
        author = {Xiao, Jinzhao and Huang, Yuxiang and Hu, Changyu and Song, Shaoxu and Huang, Xiangdong and Wang, Jianmin},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {10},
        pages = {2148--2160},
        doi = {10.14778/3547305.3547319},
        url = {https://doi.org/10.14778/3547305.3547319},
        year = {2022}
}

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