A Framework for Efficient Discovery of Web Services Across Heterogeneous Registries
Bibliographic record
Abstract
Growth and propagation of the Internet has been a contributing factor for information overload which acts as a deterrent for quick and easy discovery of information. As Web services proliferate, the same dilemma perceived in the discovery of Web pages will become tangible. Currently, the automatic discovery of Web services, an important capability of service- oriented architecture (SOA), is mainly achieved by performing inquiries to business registries such as the UDDI or ebXML. The ability to discover Web services across multiple heterogeneous registries is becoming a challenging task and raises several issues such as performance, reliability, and robustness. In this paper, we introduce the Web Service Repository Builder (WRSB) that serves as an integrated SOA registry and repository for managing the proliferation of Web services and system artifacts. Specifically, the proposed framework actively captures and navigates among multiple service registries and provides a unified environment for the discovery of Web services. The WSRB framework is compatible with, and can be integrated seamlessly into, the existing infrastructure without any modifications to the existing environments.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".