AHS Model: Efficient Topological Operators for a Sensor Web Publish/Subscribe System
Bibliographic record
Abstract
The Worldwide Sensor Web has been applied for monitoring the physical world with spatial and temporal scales that were impossible in the past. With the development of sensor technologies and interoperable open standards, sensor webs generate tremendous volumes of priceless data, enabling scientists to observe previously unobservable phenomena. With its powerful monitoring capability, the sensor web is able to capture time-critical events and provide up-to-date information to support decision-making. In order to harvest the full potential of the sensor web, efficiently processing sensor web data and providing timely notifications are necessary. Therefore, we aim to design a software component applying the publish/subscribe communication model for the sensor web. However, as sensor web data are geospatial in nature, existing topological operators are inefficient when processing a large number of geometries. This paper presents the Aggregated Hierarchical Spatial Model (AHS model) to efficiently determine topological relationships between sensor data and predefined query objects. By using a predefined hierarchical spatial framework to index geometries, the AHS model can match new sensor data with all subscriptions in a single process to improve the query performance. Based on our evaluation results, the query latency of the AHS model increases 2.5 times more slowly than that of PostGIS. As a result, we believe that the AHS model is able to more efficiently process topological operators in a sensor web publish/subscribe system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".