A New Business Model and Architecture for Context-Aware Applications Provisioning in the Cloud
Bibliographic record
Abstract
Context-aware applications are seen as one of the "killer" application categories of the future, due to their ability to offer personalized services by adapting their behavior according to the users' needs and changing situation. Context-aware applications rely in their operation on a complex set of functionalities (i.e. context-awareness substrates). In order to facilitate the development of novel context-aware applications and achieve efficiency in terms of resource utilization, there is a need for a unified, openly-accessible, scalable context management platform that enables the dynamic discovery, composition, and reuse of context-awareness substrates by various context-aware applications. The lack of such platform is a major impediment to the fast and resource efficient development of context aware applications. In this paper, we propose a novel virtualized context management platform in the cloud, in which a shared pool of virtualized context-awareness substrates can be offered by different providers, and leased on demand. Those substrates can be dynamically discovered and composed to enable fast and cost-effective development of a variety of context-aware applications. The proposed platform relies on a new business model which introduces the sensors substrate provider and the broker as new roles in the traditional cloud business model. A detailed software architecture and preliminary prototype implementation are also presented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".