Machine-Learning-Based Multidimensional Big Data Analytics over Clouds via Multi-Columnar Big OLAP Data Cube Compression
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
This paper proposes a new theory on combining innovative Multidimensional Big Data Analytics with well-known Machine Learning (ML) in order to magnify the expressive power and the accuracy of knowledge insights discovery from massive big datasets. At the level of enabling technology, with the goal of fully supporting this novel paradigm, the issue of managing and mining big OLAP data cubes over Clouds arises. Due to computational complexity requirements, the latter challenge is addressed by proposing an innovative solution for (1) representing big OLAP data cubes over Clouds via a multi-column-based representation, and (2) compressing the deriving multi-column representations for achieving the desired effectiveness and efficiency. This paper introduces the fundamental model of Machine-Learning-Based Multidimensional Big Data Analytics, along with a reference architecture implementing it.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.008 |
| 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