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
Event logs of process-aware information systems play an increasingly critical role in today's enterprises because they are the basis for a number of business intelligence applications such as complex event processing, provenance analysis, performance analysis, and process mining. However, due to incorrect manual recording, system errors, and resource constraints, event logs inevitably contain noise in the form of deviating event sequences with redundant, missing, or dislocated events. To repair event logs, existing approaches rely on predefined process models to obtain a minimum recovery for each deviating event sequence. However, process models are typically unavailable in practice, rendering existing approaches inapplicable. In this scenario, can event logs be self-healing? To address this problem, we propose an approach that leverages compliant event sequences to repair deviating sequences. Our approach is effective if the compliant event sequences contain sufficient knowledge for repair. We implement our approach in a prototype and employ the tool to conduct experiments. The experimental results demonstrate that our approach can achieve efficient repairs without the help of process models.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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