Bibliographic record
Abstract
Integrity constraints are the primary tool used to capture business rules and domain constraints in data management systems. When these constraints are not strictly enforced, poor data quality often arises, as inconsistencies occur between the data and the set of constraints. To resolve these inconsistencies, organisations often implement specific, sometimes manual, cleansing routines to fix the errors. As modern systems are expected to handle increasing amounts of highly heterogeneous data, often in dynamic data environments where the data and the constraints may change, manual cleansing routines are insufficient to handle this increased scale and heterogeneity. In this work, we present a set of new constraint repair operations that can be incorporated into a data quality tool that provides automated support for both data and constraint repair and management. Our holistic approach is designed to facilitate the curation and maintenance of both the data and the constraints. We focus on discovering trends, contextual information, and data patterns to understand how a business rule (constraint) has evolved. We also investigate how to find a minimal set of constraints that contain non-redundant information since enforcing extraneous constraints is costly and can negatively affect system performance. We conduct two case studies using real business datasets that demonstrate the quality and usefulness of our techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".