Testing of sensor observation services
Bibliographic record
Abstract
Recently, sensor webs have been increasingly used to monitor and sense a multitude of observations for various applications, from simple phenomena, such as air pollution measurements, to complex events, for instance perimeter security, or effluent tracking. Therefore, the performance of sensor data delivery mechanisms is becoming more and more important to ensure that services dependent upon sensor web technology perform satisfactorily. In the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) framework, Sensor Observation Service (SOS) is a standard web service interface responsible for requesting, filtering, and retrieving sensor observations. In this paper, we present initial results from a quantitative analysis of SOS servers' performance. To do this, we measured the response time and transferred data volume, the response size, of three SOS servers -- 52North, MapServer, and Deegree -- based on different test scenarios. The results are illustrated and discussed. Our findings can be helpful: (i) to understand how different parameters affect the SOS servers; (ii) to help SOS developers identify areas for improvement of their SOS; and (iii) to help application developers and users make informed decisions about their choice of SOS server.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".