Model Checking Data-Aware Workflow Properties with CTL-FO+
Bibliographic record
Abstract
Most works that extend workflow validation beyond syntactical checking consider constraints on the sequence of messages exchanged between services. However, these constraints are expressed only in terms of message names and abstract away their actual data content. Using the context of the User-controlled Lightpath initiative (UCLP) hosted by the CANARIE consortium, we provide examples of real- world "data-aware" web service constraints where the sequence of messages and their content are interdependent. We present CTL-FO+, an extension over Computation Tree Logic that includes first-order quantification on state variables in addition to temporal operators. We show how CTL- FO+is adequate for expressing data-aware constraints, give a complete model checking algorithm for CTL-FO+and establish its complexity to be PSPACE-complete. This makes using CTL-FO+for validating workflow properties no harder than using the Linear Temporal Logic (LTL) already used by some web service tools. Finally, we show how the modelling of data-aware properties is an increase in expressiveness that cannot be efficiently simulated by these tools.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".