Bibliographic record
Abstract
Many applications generate massive data streams. Summarizing such massive data requires fast, small space algorithms to support post-hoc queries and mining. An important observation is that such streams are rarely uniform, and real data sources typically exhibit significant skewness. These are well modeled by Zipf distributions, which are characterized by a parameter, z, that captures the amount of skew. We present a data stream summary that can answer point queries with ∊ accuracy and show that the space needed is only O(∊−-min{1,1/z}). This is the first o(1/∊) space algorithm for this problem, and we show it is essentially tight for skewed distributions. We show that the same data structure can also estimate the L2 norm of the stream in o(1/∊2) space for z > ½, another improvement over the existing Ω(1/∊2) methods. We support our theoretical results with an experimental study over a large variety of real and synthetic data. We show that significant skew is present in both textual and telecommunication data. Our methods give strong accuracy, significantly better than other methods, and behave exactly in line with their analytic bounds.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".