TsQuality: Measuring Time Series Data Quality in Apache IoTDB
Summary: TsQuality measures time-series data quality in Apache IoTDB at scale, implementing four metrics—completeness, consistency, timeliness, validity—as in-DB functions or Spark operators. Provides multi-granularity dirty-point navigation and integrates with Zeppelin/Superset for SQL-driven inspection and dashboards over millions of series. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuanhui Qiu (Tsinghua University)
- 2. Chenguang Fang (Tsinghua University)
- 3. Shaoxu Song (Tsinghua University)
- 4. Xiangdong Huang (Timecho Limited)
- 5. Chen Wang (Timecho Limited)
- 6. Jianmin Wang (Tsinghua University)
BibTeX Citation
@article{qiu_vldb23,
title = {{TsQuality: Measuring Time Series Data Quality in Apache IoTDB}},
author = {Qiu, Yuanhui and Fang, Chenguang and Song, Shaoxu and Huang, Xiangdong and Wang, Chen and Wang, Jianmin},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3982--3985},
doi = {10.14778/3611540.3611601},
url = {https://doi.org/10.14778/3611540.3611601},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,090 | T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,793 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 7.1217835e-05 |
| 5,175 | Cleanits: A Data Cleaning System for Industrial Time Series | 2019 | VLDB | 6.3332964e-05 |
| 7,314 | Time Series Data Validity | 2023 | SIGMOD | 5.6471526e-05 |
| 9,199 | On Repairing Timestamps for Regular Interval Time Series | 2022 | VLDB | 5.3058708e-05 |
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