TRADEOFFS IN GOOGLE DISTANCE ONLY VS A WORDNET HYBRID FOR QOS-ENABLED WEB SERVICE COMPOSITION
Bibliographic record
Abstract
This thesis proposes a hybrid approach in using Google Distance and WordNet together in a new method, called the Domain Independent Quality of Service (DIQOS) method for QoS-enabled web services discovery. Comparisons, using delay, recall, and precision metrics, between this hybrid approach and an earlier lightweight Google Distance-only based approach for web services discovery are provided. Further, our performance evaluation demonstrates as of yet undocumented trade-offs between Google distance, Google-WordNet distance, and WordNet distance approaches for similarity matching in the web services discovery phase. The impact of all approaches on QoS-enabled web service composition is described for representative web transactions in the travel domain. Findings include that the recall of signature matching increases by 5 to 15% for WordNet-assisted Google Distance DIQOS approach over the pure Google-distance DIQOS variant. WordNet-assisted Google Distance also shows 20-40 % increases in recall for signature matching compared to the WordNet only approach. Also, bigram-based, short sentence, and WordNet-based vector optimizations in specification similarity matching show an average of 25% increase in recall over a previous competitive method called the Flexible Ontology-Independent QOS-enabled method (FOIQOS). Our WordNet-assisted Google Distance method shows 34% increase in recall compared to FOIQOS. Our approaches produce 11% lower precision than FOIQOS, but FOIQOS is speedier as delays are 5 to 15 % lower.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".