Towards MapReduce based Bayesian deep learning network for monitoring big data applications
Bibliographic record
Abstract
One of the most commonly used ways to monitor execution of software applications is by analyzing logs. Logs are execution foot-print of software applications that are produced and stored for real-time or post-execution analysis of execution. With the software applications becoming large, complex, distributed, web-scale, also called as big data applications, logs produced by such software applications are also large-scale. That means, such logs are large in volume, velocity and variety. That makes it crucial to have such logs analyzed in an automated, scalable and effective manner to ensure high veracity and have analytics with high value. In this paper, we present our proposed solution of a formal model for organizing and structuring logs. We then present a Bayesian deep learning network based analysis approach that utilizes the formal model for logs to detect and predict any possible faults and consequences of such faults. Moreover, we also present our MapReduce based distributed, parallel, single-pass and incremental approach to build, train and execute the proposed Bayesian deep learning framework. This helps in effective processing of logs on cloud platforms and therefore efficient handling of logs that are produced at the scale of big data by big data applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".