Clean4TSDB: A Data Cleaning Tool for Time Series Databases
Summary: Clean4TSDB: end-to-end cleaning for time-series DBs, integrating expressive constraint discovery, violation detection, and multivariate repair (preconfigured for systems like Apache IoTDB). Novelty: TSDD—a mined contextual multivariate constraint—with an efficient miner and hybrid row/column repair plus a modular library of repair algorithms. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaoou Ding
- 2. Yichen Song
- 3. Hongzhi Wang
- 4. Donghua Yang
- 5. Chen Wang
- 6. Jianmin Wang
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,560 | MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data | 2024 | VLDB | 4.3254416e-05 |
| 10,211 | SHoTClean: Bridging Soft and Hard Constraints for Multivariate Time Series Cleaning | 2026 | SIGMOD | 4.1945683e-05 |
| 10,674 | Improving Time Series Data Compression in Apache IoTDB | 2025 | VLDB | 4.1945683e-05 |
| 10,723 | UniClean: A Scalable Data Cleaning Solution for Mixed Errors based on Unified Cleaners and Optimized Cleaning Workflow | 2025 | VLDB | 4.1945683e-05 |
| 10,811 | DemandClean: A Multi-Objective Learning Framework for Balancing Model Tolerance to Data Authenticity and Diversity | 2025 | VLDB | 4.1945683e-05 |
| 10,812 | TARImpute: Task-Aware auto-Recommender System for Missing Value Imputation Algorithms with Clustering Case Studies | 2025 | VLDB | 4.1945683e-05 |
| 10,855 | bNDCRepair: Cleaning both Data Errors and Inaccurate Constraints on Numerical Sequential Data | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,253 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00013032074 |
| 3,133 | Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing | 2017 | VLDB | 7.4978041e-05 |
| 3,396 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.1455126e-05 |
| 3,825 | Cleanits: A Data Cleaning System for Industrial Time Series | 2019 | VLDB | 6.7255837e-05 |
| 3,967 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 6.5796647e-05 |
| 6,583 | SCREEN: Stream Data Cleaning under Speed Constraints | 2015 | SIGMOD | 5.0027988e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,511 | The Best of Both Worlds: On Repairing Timestamps and Attribute Values for Multivariate Time Series | 2025 | SIGMOD | 4.1945683e-05 |
| 3,967 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 6.5796647e-05 |
| 9,048 | On Repairing Timestamps for Regular Interval Time Series | 2022 | VLDB | 4.4039656e-05 |
| 7,391 | Time Series Data Validity | 2023 | SIGMOD | 4.7429293e-05 |
| 10,211 | SHoTClean: Bridging Soft and Hard Constraints for Multivariate Time Series Cleaning | 2026 | SIGMOD | 4.1945683e-05 |
| 6,451 | Multivariate Time Series Cleaning under Speed Constraints | 2024 | SIGMOD | 5.0583324e-05 |
| 10,061 | Cleaning Time Series under Seasonal and Trend Constraints | 2026 | SIGMOD | 4.1945683e-05 |
| 3,825 | Cleanits: A Data Cleaning System for Industrial Time Series | 2019 | VLDB | 6.7255837e-05 |
| 9,560 | MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data | 2024 | VLDB | 4.3254416e-05 |
| 8,912 | TsQuality: Measuring Time Series Data Quality in Apache IoTDB | 2023 | VLDB | 4.427232e-05 |