Bibliographic record
Abstract
Data partitioning induces network transfers and dominates the cost of stream data analytics. Moreover, partitioning streaming data for multiple stream queries in the same cluster can easily saturate the network bandwidth and lead to high end-to-end latencies.The goal of this paper is to share the partition operation in streaming workloads and maximize the sharing opportunities for multiple stream queries. However, there are several challenges, such as minimizing data copy, optimizing the partitioning strategy for multiple queries, and minimizing latency.We propose SASPAR, Shared Adaptive Stream Partitioner, which is able to share data partitioning among multiple stream queries. Our contributions are threefold. First, we propose a new technique to optimize the partitioning strategy for multiple stream queries. Second, we present an adaptive query execution framework that performs optimizations at run-time, without stopping the query execution plan. Third, we utilize meta-heuristics and machine learning when solving the underlying optimization problem takes more time than expected.SASPAR is designed as a versatile layer to sit on top of a stream processing engine (SPE). We operate SASPAR on top of three state-of-the-art SPEs with hundreds of stream queries. Our experimental results show that SASPAR improves the performance (throughput and latency) of all underlying SPEs by up to 3x.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".