Systematic Development of Data Mining-Based Data Quality Tools
Summary: Introduces a data audit test generator that creates and perturbs synthetic benchmark databases to calibrate error measurements in ML-driven, induced-schema data quality tools. Implemented in a C4.5-based framework and validated on a DaimlerChrysler service DB; it complements standard data scrubbing. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Dominik Luebbers
- 2. Udo Grimmer
- 3. Matthias Jarke
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 67 | The Merge/Purge Problem for Large Databases | 1995 | SIGMOD | 0.00061348205 |
| 161 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.00039846974 |
| 691 | AJAX: An Extensible Data Cleaning Tool | 2000 | SIGMOD | 0.00018086135 |
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