An integrated fuzzy Delphi-DEMATEL and analytic network process for sustainable operations and evaluations in process mining
Bibliographic record
Abstract
This study examines the evolving role of process mining in monitoring real-world processes through advanced discovery algorithms like alpha, PSO miner, and genetic miner. Traditionally, these algorithms have been evaluated using precision, consistency, simplicity, and generalisation. However, the IEEE task force on process mining called for new dimensions and refined priorities. Using the fuzzy Delphi method, the study evaluated 12 dimensions and 20 criteria, narrowing them to 6 dimensions and 10 key criteria. The decision-making trial and evaluation laboratory (DEMATEL)-based analytic network process (DANP) method was applied to prioritise and weigh these factors. Key additions include the new dimensions of explainability and robustness. The study identifies consistency, cyclomatic complexity, and density as major performance influencers, with simplicity receiving the highest weight (0.175) and consistency the lowest (0.153). These findings aim to enhance process discovery algorithms by addressing both traditional and newly identified dimensions.
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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 | 0.084 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".