Selecting Representative Sample Traces from Large Event Logs
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
When event logs are large, the time needed to analyze them using process mining techniques can become prohibitive. In this paper, using sampling, we aim to reduce the size of event logs to p-traces, while minimizing the Earth Movers’ Distance (EMD) from the unsampled original event log. We contribute by formalizing log sampling in a canonical form and show its link with the EMD, a metric increasingly used for process mining. Next, we propose three log-sampling algorithms that we evaluate using a collection of 18 event logs from industry. We show that our approach largely reduces the EMD compared to existing sampling strategies. Moreover, we highlight that sampled event logs with low EMDs tend to have better behavioural quality, highlighting the generality of our work.
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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 | 0.001 |
| 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) | 0.002 | 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