LOST-Tree: a spatio-temporal structure for efficient sensor data loading in a sensor web browser
Bibliographic record
Abstract
We present LOST-Tree, a new spatio-temporal structure to manage sensor data loading and caching in a sensor web browser. In the same way that the World Wide Web needs a web browser to load and display web pages, the World-Wide Sensor Web needs a sensor web browser to access distributed and heterogeneous sensor networks. However, most existing sensor web browsers are just mashups of sensor locations and base maps that do not consider the scalability issues regarding transmitting large amounts of sensor readings over the Internet. While caching is an effective solution for alleviating the latency and bandwidth problems, a method for efficiently loading sensor data1 from sensor web servers is currently missing. Therefore, we present LOST-Tree as a sensor data loading component that also manages the client-side cache on a sensor web browser. By applying LOST-Tree, redundant transmissions are avoided, enabling efficient loading with cached sensor data. We demonstrate that LOST-Tree is lightweight and scalable, in terms of sensor data volume. We implemented LOST-Tree in the GeoCENS sensor web browser for evaluation with a real sensor web dataset.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".