Integrating heterogeneous web services from an end user perspective
Bibliographic record
Abstract
Service composition combines a set of available Web services using control flows to create a more complex service. The service composition is a complex process which is challenging even for experienced users to discover and select appropriate service among a set of similar services. The available tools and techniques for service composition either are too complicated or require technical knowledge (such as script language). Moreover the heterogeneity in service description language and communication protocols make service composition more difficult. In our research, we propose an approach that allows non-technical users (i.e., end users) to easily compose heterogeneous Web resources with different communication protocol. The goal of this research is fulfilled on two stages: 1) identifying integrable Web resources from different Web applications and SOAP-based Web services; and 2) providing approaches to discover and compose Web resources that are capable of accomplishing user's activities. We propose a framework that assists a user to discover and compose services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".