Top-down computation of partial ROLAP data cubes
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
The precomputation of the different summary views of a data cube is critical to improving the response time of data cube queries for online analytical processing (OLAP). The computation of the full data cube, representing all 2/sup d/ views, has been studied extensively. However, the full cube is often too large to be computed and stored, and for some applications all views are not even required. Hence, it is important to provide efficient methods for the computation of partial data cubes consisting of an arbitrary, user selected, subset of the 2/sup d/ possible views. In this paper, we study the top-down computation of partial ROLAP data cubes. We present both sequential and parallel methods for top-down partial data cube construction. Our experimental results indicate close to linear performance improvement for partial data cube computation. For example, when selecting 50% of the views our method requires only 55% of the time required to build the full cube, and when selecting 75% of the views our method requires just 82% of the full cube time.
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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 | 0.001 |
| Open science | 0.000 | 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