Mining common morphological fragments from process 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
Many organizations have implemented their organizational processes within integrated information systems using formal process models. These processes, which have been implemented in different organizations can share significant amount of similarities. Analysis and mining of these processes for identifying similarities can lead to valuable insight for the organizations. There has already been work on mining process models from event logs for an individual organization. The objective of this paper is, however, to detect and extract common process fragments from a family of processes that may not have been executed within the same application/organization. These identified common fragments can be used as building blocks of future applications or be used for refactoring existing applications. To this end, we first provide a precise definition of process fragments. We define morphological fragments as operationally identical fragments. We then propose an algorithm for extracting morphological fragments from process event logs. We discuss the relative performance of our proposed algorithm and its applicability in practice.
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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 | 0.001 | 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) | 0.000 | 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