Bibliographic record
Abstract
Goal-oriented process enhancement and discovery (GoPED) was recently proposed to take advantage of goal modeling capabilities in process mining activities. Conventional process mining aims to discover underlying process models from historical, crowdsourced event logs in an activity-oriented fashion. GoPED, however, infers goal-aligned process models from the event logs enhanced with some goal-related attributes. GoPED selects the historical behaviors that have yielded sufficient levels of satisfaction for (often conflicting) goals of different stakeholders. There are three algorithms available to select the subset of event logs from three different perspectives. The main input of all three algorithms is a version of the event log (EnhancedLog) that is (1) structured as a table showing each case and its trace in one row, (2) with rows enhanced with satisfaction levels of different goals. Therefore, typical event logs are not ready to be fed as-is to GoPED algorithms. This paper proposes a scheme for manipulating original event logs and turn them into EnhancedLog. Two tools were also developed and tested for this scheme: TraceMaker, to structure the log as explained above, and EnhancedLogMaker, to compute satisfaction levels of goals for all cases in the structured log.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".