Deriving Effectiveness Measures for Data Quality Rules
Bibliographic record
Abstract
The poor quality of data constitutes a major concern worldwide, and an obstacle to data integration and analysis efforts. Detecting errors and inconsistencies using application specific data quality rules play an important role in data quality assessment. These rules have different efficacy and cost under different circumstances. In our previous work, we have proposed a quantitative framework for measuring and comparing data quality rules in terms of their effectiveness. Effectiveness formulas are built from variables that represent probabilistic assumptions about the occurrence of errors in data values, and our earlier work gave examples of how to derive these formulas in an ad-hoc fashion. This paper lays the foundations of a workbench-approach for systematically deriving effectiveness formulas. The approach involves several steps, including building Bayesian network graphs, adding (symbolic) probabilities to the nodes in the graph, and deriving effectiveness formulas. The graphs are built algorithmically, for a large and useful class of data quality rules. We present this approach and its implementation in Python, and report its evaluation results, which show that the resulting formulas give reasonable estimates of effectiveness scores under various scenarios. 1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".