WSCE: A Crawler Engine for Large-Scale Discovery of Web Services
Bibliographic record
Abstract
This paper addresses issues relating to the efficient access and discovery of Web services across multiple UDDI Business Registries (UBRs). The ability to explore Web services across multiple UBRs is becoming a challenge particularly as size and magnitude of these registries increase. As Web services proliferate, finding an appropriate Web service across one or more service registries using existing registry APIs (i.e. UDDI APIs) raises a number of concerns such as performance, efficiency, end-to-end reliability, and most importantly quality of returned results. Clients do not have to endlessly search accessible UBRs for finding appropriate Web services particularly when operating via mobile devices. Finding relevant Web services should be time effective and highly productive. In an attempt to enhance the efficiency of searching for businesses and Web services across multiple UBRs, we propose a novel exploration engine, the Web Service Crawler Engine (WSCE). WSCE is capable of crawling multiple UBRs, and enables for the establishment of a centralized Web services' repository which can be used for large-scale discovery of Web services. The paper presents experimental validation, results, and analysis of the presented ideas.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".