A HYBRID PULL-PUSH SYSTEM FOR NEAR REAL-TIME NOTIFICATIONS ON SENSOR WEB
Bibliographic record
Abstract
Abstract. World-wide sensor web generates tremendous amount of sensor data stream allowing people to observe events that were previously unobservable. Sensor web has been wildly applied in many monitoring systems; some of them are extremely time-sensitive, e.g., disaster management systems. However, with the growing amount of sensor data, the traditional request/response communication model becomes inefficient as it is based on point-to-point pulling interactions between users and data providers. In order to address this issue, publish/subscribe communication model has been proposed and applied in many applications, e.g., web blogging. The publish/subscribe model utilizes an intermediary broker on matching predefined queries with the data pushed to the broker. However, we argue that the publish/subscribe model is hard to be directly applied to sensor web due to the fact that most sensor web services are based on pulling interaction model only. For instance, more and more sensor data providers are publishing their sensor data with the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS) standards, and the OGC SOS services are based on the request/response model. Therefore, in order to address this issue, we propose a hybrid pull-push system to retrieve sensor web data in a timely manner. The preliminary experimental results indicate that the proposed system is able to fetch near real time sensor streams from pull-based sensor 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".