bNDCRepair: Cleaning both Data Errors and Inaccurate Constraints on Numerical Sequential Data
Summary: bNDCRepair jointly repairs numerical sequential data and inaccurate constraints via domain expand/compress operations and constraint-modification functions to avoid under-/overfitting. Theoretically bounded optimality; +17.6% F1 and best MNAD vs. baselines. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Xiaoou Ding
- 2. Muyun Zhou
- 3. Yida Liu
- 4. Chen Wang
- 5. Hongzhi Wang
- 6. Jianmin Wang
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 621 | Improving Data Quality: Consistency and Accuracy | 2007 | VLDB | 0.00018978331 |
| 3,198 | Towards Dependable Data Repairing with Fixing Rules | 2014 | SIGMOD | 7.4029546e-05 |
| 10,081 | From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies | 2026 | SIGMOD | 4.1905499e-05 |
| 3,397 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.1386386e-05 |
| 3,142 | Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing | 2017 | VLDB | 7.4905759e-05 |
| 9,560 | MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data | 2024 | VLDB | 4.3212967e-05 |
| 2,417 | Combining Quantitative and Logical Data Cleaning | 2016 | VLDB | 8.8502556e-05 |
| 9,380 | Constraint-Variance Tolerant Data Repairing | 2016 | SIGMOD | 4.3439402e-05 |
| 6,447 | Multivariate Time Series Cleaning under Speed Constraints | 2024 | SIGMOD | 5.0534784e-05 |
| 4,991 | Sequential Data Cleaning: A Statistical Approach | 2016 | SIGMOD | 5.7754141e-05 |