On the Fly Service Composition for Local Interaction Environments
Bibliographic record
Abstract
Dynamically creating new, composite services "on the fly" using existing ones in a local interaction environment (e.g. a home, meeting room, airport lounge, etc.) presents challenging problems. In this paper, we describe a middleware model for such service composition targeted, initially, to a home area network (HAN) scenario. Being able to automatically synthesize new and useful services in a HAN (or other environment) without user intervention makes the use of the network simpler and more attractive for non-expert users (e.g. home owners). We propose a service composition model which involves third-party "service providers" (SPs) in the composition process thereby allowing the discovery of services without direction from the users as to what type of service is desired. We also discuss our experiences with our initial prototype system where ontology based matching is done by the service providers and the resulting composite service is deployed as a workflow using available in-home protocols (e.g. UPnP, Jini, etc.)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".