Context-Aware Generic Service Discovery and Service Composition
Bibliographic record
Abstract
Service Composition is to create a new business process by composing several services in order to fulfill business goals that individual services cannot achieve. Service discovery and service composition can be highly adaptive to contexts, i.e., according to context information, e.g., location, budget and time, we can discover and compose these services to satisfy particular requirements in the contexts. Moreover, we want to contain non-electronic services, e.g., restaurants, into service composition. These services are not considered in existing service composition research, but are ubiquitous in mobile phone working environment. In this paper, we present the methods of discovering and composing non-electronic services based on contexts. We build a constraint-based context model which is more suitable for service composition algorithm than the other context model. Our service composition algorithm uses soft constraints. With this feature, the service composition algorithm can give the user several "good enough" solutions, instead of null solution. More importantly, we include non-electronic services into service composition. Throughout the paper, an entertainment planner is used as a motivated example.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".