Methodology and Tool for Business Process Compensation Design
Why this work is in the frame
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Bibliographic record
Abstract
A typical e-business transaction takes hours or days to complete, involves a number of partners, and comprises many failure points. With short-lived transactions, database systems ensure atomicity by either committing all of the elements of the transaction, or by canceling all of them in case of a failure. With typical e-business transactions, strict atomicity is not practical, and we need a way of reversing the effects of those activities that cannot be rolled back: that is compensation. For a given business process, identifying the various failure points, and designing the appropriate compensation processes represents the bulk of process design effort. Yet, business analysts have little or no guidance. For a given failure point, there appears to be an infinite variety of ways to compensate for it. We recognize that compensation is a business issue, but we argue that it can be explained in terms of a handful of parameters within the context of the REA ontology, including things such as the type of activity, the type of resource, and organizational policies. We propose a three-step compensation design approach that 1) starts by abstracting a business process to focus on those activities that create/modify value, 2) compensates for those activities, individually, based on values of the compensation parameters, and 3) composes those compensations using a Saga-like approach. In this paper, we present our approach along with an implementation algorithm and propose a business ontology for compensation design.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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