Scalable Automatic Service Composition using Genetic Algorithms
Bibliographic record
Abstract
A composition of simple web services, each dedicated to performing a specific sub- task involved, proves to be a more competitive solution than an equivalent atomic web service for a complex requirement comprised of several sub-tasks. Composite services have been extensively researched and perfected in many aspects for over two decades, owing to benefits such as component re-usability, broader options for composition requesters, and the liberty to specialize for component providers. However, most studies in this field must acknowledge that each web service has a limited context in which it can successfully perform its tasks, the boundaries defined by the internal constraints imposed on the service by its providers. The restricted context-spaces of all such component services define the contextual boundaries of the composite service as a whole when used in a composition, making internal constraints an essential factor in composite service functionality. Due to their limited exposure, no systems have yet been proposed on the large-scale solution repository to cater to the specific verification of internal constraints imposed on components of a composite service. In this thesis, we propose a scalable automatic service composition capable of not only automatically constructing context-aware composite web services with internal constraints positioned for optimal resource utilization but also validating the generated compositions on a large-scale solution repository using the General Intensional Programming System (GIPSY) as a time- and cost-efficient simulation/execution environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".