Sliding Window Top-K Monitoring over Distributed Data Streams
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
Most of the traditional top-k algorithms are based on a single-server setting. They may be highly inefficient and/or cause huge communication overhead when applied to a distributed system environment. Therefore, the problem of top-k monitoring in distributed environments has been intensively investigated recently. This paper studies how to monitor the top-k data objects with the largest aggregate numeric values from distributed data streams within a fixed-size monitoring window W, while minimizing communication cost across the network. We propose a novel algorithm, which adaptively reallocates numeric values of data objects among distributed nodes by assigning revision factors when local constraints are violated and keeps the local top-k result at distributed nodes in line with the global top-k result. We also develop a framework that combines a distributed data stream monitoring architecture with a sliding window model. Based on this framework, extensive experiments are conducted on top of Apache Storm to verify the efficiency and scalability of the proposed algorithm.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.017 |
| Open science | 0.011 | 0.013 |
| 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