Results in Workflow Resiliency: Complexity, New Formulation, and ASP Encoding
Bibliographic record
Abstract
First proposed by Wang and Li in 2007, workflow resiliency is a policy analysis for ensuring that, even when an adversarial environment removes a subset of workers from service, a workflow can still be instantiated to satisfy all the security constraints. Wang and Li proposed three notions of workflow resiliency: static, decremental, and dynamic resiliency. While decremental and dynamic resiliency are both PSPACE-complete, Wang and Li did not provide a matching lower and upper bound for the complexity of static resiliency. The present work begins with proving that static resiliency is $Π_2^p$-complete, thereby bridging a long-standing complexity gap in the literature. In addition, a fourth notion of workflow resiliency, one-shot resiliency, is proposed and shown to remain in the third level of the polynomial hierarchy. This shows that sophisticated notions of workflow resiliency need not be PSPACE-complete. Lastly, we demonstrate how to reduce static and one-shot resiliency to Answer Set Programming (ASP), a modern constraint-solving technology that can be used for solving reasoning tasks in the lower levels of the polynomial hierarchy. In summary, this work demonstrates the value of focusing on notions of workflow resiliency that reside in the lower levels of the polynomial hierarchy.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".