Methodology and Tool for Business Process Compensation Design
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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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".