Dependable Data Repairing with Fixing Rules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the main challenges that data-cleaning systems face is to automatically identify and repair data errors in a dependable manner. Though data dependencies (also known as integrity constraints) have been widely studied to capture errors in data, automated and dependable data repairing on these errors has remained a notoriously difficult problem. In this work, we introduce an automated approach for dependably repairing data errors, based on a novel class of fixing rules . A fixing rule contains an evidence pattern, a set of negative patterns, and a fact value. The heart of fixing rules is deterministic : given a tuple, the evidence pattern and the negative patterns of a fixing rule are combined to precisely capture which attribute is wrong, and the fact indicates how to correct this error. We study several fundamental problems associated with fixing rules and establish their complexity. We develop efficient algorithms to check whether a set of fixing rules are consistent and discuss approaches to resolve inconsistent fixing rules. We also devise efficient algorithms for repairing data errors using fixing rules. Moreover, we discuss approaches on how to generate a large number of fixing rules from examples or available knowledge bases. We experimentally demonstrate that our techniques outperform other automated algorithms in terms of the accuracy of repairing data errors, using both real-life and synthetic data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.059 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it