Web service composition ranking based on QoS and social network analysis
Bibliographic record
Abstract
Web service composition refers to the aggregation of web services for producing composite solutions in order to satisfy user requirements which can't be satisfied by atomic services. It is an essential challenge to find the most reliable and trustable complex services in each composition process. Many of current composition approaches use QoS (Quality of Service) values to select among different composition candidates. However, QoS values can't be trusted all the time since some service providers may promote their services by publishing wrong QoS values. Social network analysis techniques such as PageRank have been used successfully in finding the trustable and authoritative web resources. We believe that these techniques can also be used to improve the service composition process. We have developed a modified PageRank algorithm called Service Rank in order to find the importance level of each service in a composition based on its connectivity and invocation history. This can be accomplished by assigning higher weights to links which have more number of invocations, more up-to-date invocation time and contract signing time, and longer contract durations. Eventually the Service Rank score will be combined with the QoS score for composition ranking. Preliminary results from our experiments have proved the effectiveness of this method. As a consequence users can be more satisfied with the service composition result.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".