Name
Abstract | ||
|---|---|---|
The great amount of time series generated by machines has enormous value in intelligent industry. Knowledge can be discovered from high-quality time series, and used for production optimization and anomaly detection in industry. However, the original sensors data always contain many errors. This requires a sophisticated cleaning strategy and a well-designed system for industrial data cleaning. Motivated by this, we introduce Cleanits, a system for industrial time series cleaning. It implements an integrated cleaning strategy for detecting and repairing three kinds of errors in industrial time series. We develop reliable data cleaning algorithms, considering features of both industrial time series and domain knowledge. We demonstrate Cleanits with two real datasets from power plants. The system detects and repairs multiple dirty data precisely, and improves the quality of industrial time series effectively. Cleanits has a friendly interface for users, and result visualization along with logs are available during each cleaning process.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.14778/3352063.3352066 | PVLDB |
Field | DocType | Volume |
Data mining,Computer science,Database | Journal | 12 |
Issue | ISSN | Citations |
12 | 2150-8097 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoou Ding | 1 | 2 | 2.38 |
| Hongzhi Wang | 2 | 421 | 73.72 |
| Jiaxuan Su | 3 | 2 | 2.05 |
| Zijue Li | 4 | 1 | 0.69 |
| Jianzhong Li | 5 | 63 | 24.23 |
| Hong Gao | 6 | 1086 | 120.07 |