A partition-based approach to support streaming updates over persistent data in an active datawarehouse
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
Active warehousing has emerged in order to meet the high user demands for fresh and up-to-date information. Online refreshment of the source updates introduces processing and disk overheads in the implementation of the warehouse transformations. This paper considers a frequently occurring operator in active warehousing which computes the join between a fast, time varying or bursty update stream S and a persistent disk relation R, using a limited memory. Such a join operation is the crux of a number of common transformations (e.g., surrogate key assignment, duplicate detection etc) in an active data warehouse. We propose a partition-based join algorithm that minimizes the processing overhead, disk overhead and the delay in output tuples. The proposed algorithm exploits the spatio-temporal locality within the update stream, and improves the delays in output tuples by exploiting hot-spots in the range or domain of the joining attributes, and at the same time shares the I/O cost of accessing disk data of relation R over a volume of tuples from update stream S. We present experimental results showing the effectiveness 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 | 0.000 | 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 | 0.003 |
| 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