Scaling Up Web Service Composition with the Skyline Operator
Bibliographic record
Abstract
Web service composition enables the provision of existing resources on the web without investing in new infrastructure. However, searching an optimal composition solution with both functional and non-functional requirements is a computationally demanding problem: the time and space requirements may be insufferable due to the high number of available services. To alleviate this problem, we propose the application of a skyline operation to reduce the search space and improve the scalability. We design a system to solve the composition problem with two separate processes. The Graphplan approach finds a solution in a short time, the database approach may take longer time to find a solution, but the solution returned by this approach always has fewer redundant services with a better QoS value. Full Solution Indexing using Database (FSIDB) approach pre-computes all services combinations and store them as paths in a database. Partial pre-composing approach chooses "popular" paths generated by FSIDB approach and store them in a separate table. If the problem can be solved by these paths, there is no need to search the table with whole paths. We evaluate our approach with a web service challenge dataset.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".