Achieving Survivability in Business Process Execution Language for Web Services (BPEL) with Exception-Flows
Bibliographic record
Abstract
Survivability is defined as the capability of a service to fulfill its mission in a timely manner, even in the presence of attacks, failures, or accidents. Because of the severe consequences of failure, organizations are focusing on service survivability as a key risk management strategy for business processes. There are three key survivability properties: resistance, recognition, and recovery. Recovery, a hallmark of survivability, is the capability to maintain critical components and resource during attack, limit the extent of damage, and restore full services following attack. Exception handling is a way to deals with the recovery aspect of survivability. Business process execution language for Web services (BPEL) has been proposed for formal specification of business processes and interaction protocols. BPEL defines an interoperable integration model that facilitates expansion of automated process integration in both intra- and intercorporate environments. A business process description requires the specification of both the normal flow and the possible variations due to exceptional situations that can be anticipate and monitored. This paper bridges the analysis of business process survivability and its recovery aspect in terms of exception handling in the context of BPEL. The feasibility of the proposed model is demonstrated using an illustrative travel reservation example.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".