A Systematic Review and Assessment of Aspect-oriented Methods Applied to Business Process Adaptation
Bibliographic record
Abstract
Abstract — Today’s ever-changing business environments, comprised among other things of customer expectations, market demands, and legal obligations, require dynamic and adaptive business processes. Hence, enterprises need to monitor and improve their business processes against their business goals and constraints. Aspect-oriented development is known to have helped designers cope with changing con-cerns in software, even dynamically. In this paper, we per-form a systematic literature review of aspect-oriented approaches for business process adaptation. We observe that current methods focus on i) composing and swapping services based on Quality of Service (QoS), cost, rules, poli-cies, and constraints, as well as in the event of failure, ii) extracting roles and crosscutting concerns from composite services, iii) customizing process instances based on user profiles or Service Level Agreements, iv) adapting service composition and collaboration policies, and v) using moni-toring aspects to detect undesired situations. This review also suggests that our own aspect-oriented process modeling and adaptation framework is novel because none of the other approaches considers organization goals, performance and constraints as a whole when improving business proc-esses. In addition, given much prior research on aspect-oriented service composition is available, we are confident that our modeling framework is realizable.
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".