MapReduce Based Classification for Fault Detection in Big Data Applications
Bibliographic record
Abstract
Recently emerging software applications are large, complex, distributed and data-intensive, i.e., big data applications. That makes the monitoring of such applications a challenging task due to lack of standards and techniques for modeling and analysis of execution data (i.e., logs) produced by such applications. Another challenge imposed by big data applications is that the execution data produced by such applications also has high volume, velocity, variety, and require high veracity, value. In this paper, we present our monitoring solution that performs real-time fault detection in big data applications. Our solution is two-fold. First, we prescribe a standard model for structuring execution logs. Second, we prescribe a Bayesian classification based analysis solution that is MapReduce compliant, distributed, parallel, single pass and incremental. That makes it possible for our proposed solution to be deployed and executed on cloud computing platforms to process logs produced by big data applications. We have carried out complexity, scalability, and usability analysis of our proposed solution that how efficiently and effectively it can perform fault detection in 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".