A nodes scheduling model based on Markov chain prediction for big streaming data analysis
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Bibliographic record
Abstract
Summary Streaming data analysis is an important part of big data processing. However, streaming data is difficult to be analyzed and processed in real time because of the rapid data arriving speed and huge size of data set in stream model. The paper proposes a nodes scheduling model based on Markov chain prediction for analyzing big streaming data in real time by following three steps: (i) construct data state transition graph using Markov chain to predict the varying trend of big streaming data; (ii) choose appropriate cloud computing nodes to process big streaming data depending on the predicted result of the data state transition graph; and (iii) assign big streaming data to these computing nodes using the load balancing theory, which ensures that all subtasks are accomplished synchronously. Experiments demonstrate that the proposed scheduling algorithm can fast process big streaming data effectively. Copyright © 2014 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it