Combining variability, RCA and feature model for context-awareness
Bibliographic record
Abstract
As the notion of context-awareness evolves in different paradigms, the development of context-aware systems involves several processes. These processes include, inter alia, context modeling and reasoning as well as adaptation. In [1], we presented an approach that consists of context modeling and reasoning hand in hand based on relational concept analysis and descriptive logic, respectively. An essential aspect that is often neglected in context modeling and also triggers adaptation is the context variability. In this paper, we propose an approach that lies to answer this matter based on software product line. Our approach creates a semantic link between a context RCA-based model and a feature model, and uses the MAPE-K adaptation loop to determine the appropriate SPL configurations to deploy with regards to context changes. Thus, we used ontology to represent a combined context and feature model. Thereafter, the reasoning is done via descriptive logic. We also defined context rules based on SWRL and applied them to valid SPL configurations. Furthermore, we implemented the MAPE-K adaptation loop with Prolog.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".