Dependence-Based Data-Aware Process Conformance Checking
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
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.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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