On Repairing Timestamps for Regular Interval Time Series
Summary: Formalizes timestamp repair problem for regular-interval time series; proposes exact pruning-based methods and a bi-directional DP approximation. Demonstrates higher repair accuracy, enables data-quality measures, and is implemented in Apache IoTDB. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chenguang Fang (Tsinghua University)
- 2. Shaoxu Song (Tsinghua University)
- 3. Yinan Mei (Tsinghua University)
BibTeX Citation
@article{fang_vldb22,
title = {{On Repairing Timestamps for Regular Interval Time Series}},
author = {Fang, Chenguang and Song, Shaoxu and Mei, Yinan},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {9},
pages = {1848--1860},
doi = {10.14778/3538598.3538607},
url = {https://doi.org/10.14778/3538598.3538607},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,634 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 7.1266347e-05 |
| 7,198 | Frequency Domain Data Encoding in Apache IoTDB | 2023 | VLDB | 5.5727369e-05 |
| 7,554 | Time Series Representation for Visualization in Apache IoTDB | 2024 | SIGMOD | 5.4800579e-05 |
| 9,277 | TsQuality: Measuring Time Series Data Quality in Apache IoTDB | 2023 | VLDB | 5.1860131e-05 |
| 9,349 | Apache TsFile: An IoT-native Time Series File Format | 2024 | VLDB | 5.1763069e-05 |
| 10,172 | Optimizing Time Series Queries with Versions | 2024 | SIGMOD | 5.0559291e-05 |
| 11,178 | In-Database Time Series Clustering | 2025 | SIGMOD | 4.9605343e-05 |
| 11,268 | The Best of Both Worlds: On Repairing Timestamps and Attribute Values for Multivariate Time Series | 2025 | SIGMOD | 4.9605343e-05 |
| 11,319 | SimRN: Trajectory Similarity Learning in Road Networks based on Distributed Deep Reinforcement Learning | 2025 | VLDB | 4.9605343e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 190 | A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification | 2005 | SIGMOD | 0.00025812169 |
| 229 | Robust and Fast Similarity Search for Moving Object Trajectories | 2005 | SIGMOD | 0.00023850907 |
| 348 | Discovering Denial Constraints | 2013 | VLDB | 0.00020216677 |
| 1,314 | Computing Optimal Repairs for Functional Dependencies | 2018 | PODS | 0.000110547 |
| 1,644 | Apache IoTDB: Time-series Database for Internet of Things | 2020 | VLDB | 9.9773086e-05 |
| 6,322 | SCREEN: Stream Data Cleaning under Speed Constraints | 2015 | SIGMOD | 5.7959179e-05 |
| 6,574 | Mining Significant Semantic Locations From GPS Data | 2010 | VLDB | 5.7260784e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,912 | Clean4TSDB: A Data Cleaning Tool for Time Series Databases | 2024 | VLDB |
| 2 | 10,690 | Analyzing Deviations from Monotonic Trends through Database Repair | 2026 | SIGMOD |
| 3 | 11,748 | Grouping Time Series for Efficient Columnar Storage | 2023 | SIGMOD |
| 4 | 11,178 | In-Database Time Series Clustering | 2025 | SIGMOD |
| 5 | 10,629 | Cleaning Time Series under Seasonal and Trend Constraints | 2026 | SIGMOD |
| 6 | 7,486 | Time Series Data Validity | 2023 | SIGMOD |
| 7 | 2,985 | Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing | 2017 | VLDB |
| 8 | 10,644 | From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies | 2026 | SIGMOD |
| 9 | 11,268 | The Best of Both Worlds: On Repairing Timestamps and Attribute Values for Multivariate Time Series | 2025 | SIGMOD |
| 10 | 12,423 | Cleaning Timestamps with Temporal Constraints | 2016 | VLDB |