MEWSE: multi-engine workflow submission and execution on apache YARN
Bibliographic record
Abstract
In this era of BigData, designing a workflow to gain insights from the vast amount of data has become more complex.There are several different frameworks which individually process the batch and streaming data but coordinating the jobs between the engines in the workflow creates a performance penalty and other performance issues.Current workflow systems typically run only on one engine and do not offer the versatility required for today's workflows.The process of submitting the jobs on different engines manually is not only time consuming, but also requires the expertise of working on these engines.In this thesis, we have overcome the above mentioned issues by proposing a MEWSE -Multi Engine Workflow Submission and Execution on Apache YARN.It should also have design with plug and play functionalities to allow the inclusion of new engines.MEWSE has been tested on Amazon EC2 with a sample workflow which requires the following engines, Hadoop, Mahout, java and some scripts to process the data.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".