Discovering Architecture Formalism of Geo-Located Web Services for Next Generation of Mobile Networks
Bibliographic record
Abstract
Geo-located web services are web services offered in a particular geographical region. In mobile application design, a geo-located web service can be mapped to a set of mobile network location areas. As a mobile client roams in a mobile network, if he has a geo-located web service in execution progress at a supplier application server (SAS), he will lost its session in case when its current location is not covered by this SAS. With the next generation (third and up) of mobile networks, the geographical position of a mobile client will be sent back by a LoCation Server (LCS) to an application which requests it. As many geo-located web services will be deployed in the future, the great challenge for a mobile client will be to discover and maintain a geo-located web service when he is roaming. We propose a new system named Geo-Located Web Service Architecture (GLWSA) that aims to discover and maintain a geo-located web service with or without QoS at the nearest SAS of a mobile client current location. The GLWSA is a set of discover servers named GLWSMs (Geo-Located Web Service Manager) which are distributed in the topology. The GLWSA extends the UDDI and MLP protocols to add the GLWSM topology management and the thematic location of a mobile clients group, respectively. A thematic location consists of sending to a LCS, a chain of characters that represents a theme or a subject linking a group of mobile clients. In this paper, we present the GLWSA concepts and its mathematical formalism. Tests executed to evaluate the system performance prove that the GLWSA concepts are adequate to discover geo-located web services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".