Bibliographic record
Abstract
Big data workload characterization is an inevitable part of big data workload prediction and auto-tuning big data applications.Due to many different ways of applying big data frameworks and applications, there are various categories that these workloads can belong.Methods such as classifications that employ historical records are not beneficial since they require humans to assemble training sets for these workloads in order to build a model, which is ultimately expensive.Consequently, it is necessary to classify these workload streams automatically and without human intervention.Clustering techniques are applied in this research to detect Apache Spark and Hadoop workloads independent of historical data.Clustering techniques are compared in terms of different evaluation metrics, and the ones with the highest performance are introduced.The DBSCAN algorithm has shown the best performance and adequacy with 71% and 80% for the Purity, and Windows Type Accuracy (Awt), respectively.Ultimately, the Incremental DBSCAN algorithm and Den-Stream (an online version of DBSCAN) are presented as the most practical methods for big data workload discovery automatization.A scheme is then provided to use these algorithms integrated with methods to self-discover their hyperparameters.Ultimately, the procedure is fully automated.The proposed prototype automatically discovers the Apache Hadoop and Spark workloads' data stream, with a high degree of accuracy, same as those identified by an expert human practitioner.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".