Quality Model for Evaluating and Choosing a Stream Processing Framework Architecture
Bibliographic record
Abstract
Today, we have to deal with many data (Big data) and we need to make decisions by choosing an architectural framework to analyze these data coming from different area. Due to this, it becomes problematic when we want to process these data, and even more, when it is continuous data. When you want to process some data, you have to first receive it, store it, and then query it. This is what we call Batch Processing. It works well when you process big amount of data, but it finds its limits when you want to get fast (real-time) processing results, such as financial trades, sensors, user session activity, etc. The solution to this problem is stream processing. Stream processing approach consists of data arriving record by record, and rather than storing it, the processing is done as the data arrive. In this paper, we propose an assessment quality model to evaluate and choose stream processing frameworks. We describe briefly different architectural frameworks such as Spark Streaming, Storm, Flink and Samza that address the stream processing. Using our quality model, we present a decision tree to support engineers to choose a framework following the quality aspects. Finally, we evaluate our model doing a case study to Twitter and Netflix streaming; model that will serve as well for engineers, as for future framework designers.
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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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".