Bibliographic record
Abstract
The growing number of Services on the Web has made locating desired Web Services a sizable challenge. Web Service requestors deem a Quality of Service (QoS) based Web Service selection important in terms of providing a relevant and user centric service selection experience. In this thesis an interactive QoS based Web Service browsing mechanism is proposed, which makes use of three clustering algorithms including vector-based, preference-based and weighted clustering. We use symbolic interval data as the principle representation of QoS attributes. The browsing mechanism which was implemented as part of this research allows service requestors to prioritize their search by hierarchically clustering their web services. This is done in order of their preferences and also by attaching a weight to each QoS attribute, which is a beneficial compromise between performance-high preference-based clustering and time-efficient vector-based clustering, Along with several extensive experiments, a user study was conducted in order to test the usability of this browsing mechanism and to test the overall efficiency and performance of the three clustering algorithms in comparison. The result of the experiment led to evidences that preference-based browsing approach was the most efficient one when compared to vector-based or weighted clustering approaches.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".