<i>$\mathsf{streamline}$</i>: Accelerating Deployment and Assessment of Real-Time Big Data Systems
Bibliographic record
Abstract
Real-time stream processing applications (e.g., IoT data analytics and fraud detection) are becoming integral to everyday life. A robust and efficient Big Data system, especially a streaming pipeline composed of producers, brokers, and consumers, is at the heart of the successful deployment of these applications. However, their deployment and assessment can be complex and costly due to the intricate interactions between pipeline components and the reliance on expensive hardware or cloud environments. Thus, we proposestreamline, an agile, efficient, and dependable framework as an alternative to assess streaming applications without requiring a hardware testbed or cloud setup. To simplify the deployment, prototyping, and benchmarking of end-to-end stream processing applications involving distributed platforms (e.g., Apache Kafka, Spark, Flink), the framework provides a lightweight environment with a developer-friendly, high-level API for dynamically selecting and configuring pipeline components. Moreover, the modular architecture ofstreamlineenables developers to integrate any required platform into their systems. The performance and robustness of a deployed pipeline can be assessed with varying network conditions and injected faults. Furthermore, it facilitates benchmarking event streaming platforms like Apache Kafka and RabbitMQ. Extensive evaluations of various streaming applications confirm the effectiveness and dependability ofstreamline.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.024 |
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".