A Performance Evaluation of Distributed Framework for Mining Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we introduce a comprehensive framework for extracting and mining sensor data. This framework consists of a new formulation for the association rules, distributed extraction mechanism, and a compressed structure for the data along with the mining algorithm that is able to extract the knowledge out of it. The new formulation define the temporal relations between sensors and map them to the association rules, a well know data mining technique, a direct application of the extracted relations is predicting the sources of future events, estimating the value of missed events, or identifying faulty nodes. The proposed distributed extraction is designed to improve the network life time by reducing number of messages needed to generate the required data for the mining process, experiments have shown that our distributed extraction solution is able to reduce number of exchanged messages by 50% compared to a centralized solution. The compressed representation structure, which we call it positional lexicographic tree (PLT), is able to partition and compressed the data and provides an easy access mechanism for manipulating the data, we successfully compared the mining process of the PLT with the FP-Growth, a well know mining algorithm, results have shown that PLT outperform FP-Growth in both CPU time and memory usage
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".