Developing XML Web services with WebSphere Studio Application Developer
Bibliographic record
Abstract
Web services have recently emerged as a powerful technology for integrating heterogeneous applications over the Internet. The widespread adoption of Web services promises to usher in an exciting new generation of advanced distributed applications. These will support a new and growing set of specifications, such as Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL), and Universal Description, Discovery, and Integration (UDDI). Extensible Markup Language (XML) and its associated family of standards also play a central role in Web services by providing a data interchange format that is independent of both programming languages and operating systems. The application developer seeking to reap the benefits of Web services is therefore faced with a significant, and potentially steep, new learning curve. Clearly, application development tools that lower this barrier are crucial for the rapid and widespread adoption of Web services. This paper discusses the development tasks associated with XML Web services and describes a new suite of tools that improve developer productivity, by reducing the requirements for detailed knowledge of the underlying specifications and standards, and allow the developer to focus on the business problem domain. This suite of XML and Web services tools is part of IBM's recently released WebSphere® Studio Application Developer product, which is based on the new Eclipse open source tool integration platform.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.018 |
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".