Big Data Resource Management & Networks: Taxonomy, Survey, and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Big Data (BD) platforms have a long tradition of leveraging trends and technologies from the broader computer network and communication community. For several years, dedicated servers of homogeneous clusters were employed as the dominant paradigm in BD networks. In recent years, the BD landscape has changed, porting different deployment architectures with various network models. This trend has resulted in various associated opportunities and challenges that induce BD practitioners to achieve the next-generation BD vision. In particular, addressing the BD velocity with batch and micro-batch processing. Nevertheless, the literature misses an extensive study of the associated impacts of adopting these new deployment architectures, giving it holds a significant research interest. This study addresses the previous concern, offering a comprehensive review of the architectural elements of BD batch query deployment models and environments. A novel taxonomy is proposed to classify these models based on their underlying communication systems. We first discuss the batch query processing requirements as comparison criteria of BD communication models and compare their salient features. The benefits/challenges of these environments away from BD traditional on-premise dedicated clusters are presented. Thereafter, we provide an extensive survey of the modern BD deployment architectures, categorizing them based on their underlying infrastructure. Finally, several directions are outlined for future research on improving the state-of-the-art of BD landscape and provide recommendations for the BD practitioners on emerging environments supporting BD applications and the general large-scale data analytics.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.008 |
| 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