Dependence-Based Data-Aware Process Conformance Checking
Bibliographic record
Abstract
Data-aware executable processes are an effective and efficient means to build service-oriented applications. However, since the services involved are loosely-coupled and self-managed, the process is flexible by nature and it executions may deviate from their specifications. In contrast to existing approaches that focus on control flow deviations, we leverage activity dependences for data-aware process conformance checking. To analyze the conformance of a process instance to its process definition, we seek a process reference trace “best-fitting” the instance trace such that the conformance degree of the input trace to the process equals the consistency degree of both traces. We measure the consistency between two traces based on their activity dependences. Since finding the reference trace is NP-hard, we resort to heuristics based on process decomposition and trace replaying to determine the trace. Our approach can identify conformance decrease caused by activity dependence deviations, thus, complementing existing approaches. We implement our approach as a ProM plugin. Experimental results on 102 real-world WS-BPEL processes and 26,880 synthetic input traces confirm the effectiveness and efficiency of our approach.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".