Mapping the responses of RESTful services based on their values
Bibliographic record
Abstract
The distributed nature of service-oriented architectures imposes some very interesting challenges to the participants of a service system, i.e., the provider and the client. For example, the service may change in a way that no longer satisfies the client's needs, either due to its reduced offered functionality or quality, due to its reduced availability or due to its increased price. In this case, the client may seek to replace the consumed service with another from a competitive provider. The client will also have the challenging task of mapping the elements of the old service to those of the new service, in order to apply the appropriate changes to the client application. In this work, we propose a novel approach to perform this mapping based on the data exchanged by the service and the application (i.e., the values of the input and the output parameters of the service). This technique allows us to avoid any potential ambiguities in the vocabulary or the structure of service interfaces between different vendors. Eventually, we evaluate the performance of our mapping technique on different services from two domains, namely movie and geolocation 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".