An Efficient Qos-Based Ranking Model for Web Service Selection with Consideration \of User's Requirement
Bibliographic record
Abstract
The power of Web services to address the incompatibility issue of standalone systems, has led them to play a major role in business application development. Adopting an efficient and effective method to locate and select desired services among thousands of available candidates is an important task in the service-oriented computing. As part of a Web service discovery system, the ranking process enables users to locate their desired services more effectively. Many of the existing approaches ignore the role of user's requirements which is an important factor in the ranking process. In this thesis we enhance a vector-based ranking method by considering user's requirements. The vector-based model is chosen because of its simplicity and high efficiency. We evaluate all Web services in terms of their similarity degrees to the optimal or the best available values of each quality attribute, and penalize the services that fail to meet the user's requirements. Through our extensive experiments using real datasets, we compare the improved algorithm with other approaches to evaluate it in terms of efficiency (the execution time to return the result) and quality of the results (accuracy). Cherie Ding
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".