<i>$\mathsf{streamline}$</i>: Accelerating Deployment and Assessment of Real-Time Big Data Systems
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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 propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">streamline</i>, 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 of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">streamline</i> enables 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 of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">streamline</i>.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it