A Model Traceability Framework for Network Service Management
Bibliographic record
Abstract
Automating enactment along with traceability management of processes using model-driven engineering methods could be of significant benefit to the Network Functions Virtualization (NFV) paradigm in view of its move towards zero-touch automation of the orchestration and management of network services (NS). Earlier, we proposed an integrated process modelling and enactment environment with traceability support, MAPLE-T, for NS management. In this paper, we extend MAPLE-T with the notion of intents. We propose the usage of intents at both the process model (PM) and model-transformation levels as part of our traceability information. We define intents as information representing the objective of the PM actions/activities and their implementations. We extend MAPLE-T with traceability visualization support to visualize trace links relating models at different levels through the captured intents. The intent-enriched traceability information and the enhanced visualization enable semantically richer traceability analysis. We apply our traceability generation and analysis approach to the NS design process in order to show the benefits of intents not only for the process, but also for the whole NS lifecycle management operations.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".