Data Quality Through Active Constraint Discovery and Maintenance
Bibliographic record
Abstract
Although integrity constraints are the primary means for enforcing data integrity, there are cases in which they are not defined or are not strictly enforced. This leads to inconsistencies in the data, causing poor data quality. In this thesis, we leverage the power of constraints to improve data quality. To ensure that the data conforms to the intended application domain semantics, we develop two algorithms focusing on constraint discovery. The first algorithm discovers a class of conditional constraints, which hold over a subset of the relation, under specific conditional values. The second algorithm discovers attribute domain constraints, which bind specific values to the attributes of a relation for a given domain. These two types of constraints have been shown to be useful for data cleaning.\n\nIn practice, weak enforcement of constraints often occurs for performance reasons. This leads to inconsistencies between the data and the set of defined constraints. To resolve this inconsistency, we must determine whether it is the constraints or the data that is incorrect, and then make the necessary corrections. We develop a repair model that considers repairs to the data and repairs to the constraints on an equal footing. We present repair algorithms that find the necessary repairs to bring the data and the constraints back to a consistent state. Finally, we study the efficiency and quality of our techniques. We show that our constraint discovery algorithms find meaningful constraints with good precision and recall. We also show that our repair algorithms resolve many inconsistencies with high quality repairs, and propose repairs that previous algorithms did not consider.
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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 | 0.027 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".