Computing data cubes without redundant aggregated nodes and single graph paths: the sequential MCG approach
Bibliographic record
Abstract
In this paper, we present a novel full cube computation and representation approach, named MCG. A data cube can be defined as a lattice of cuboids. In our approach, each cuboid is seen as a set of sub-graphs. Redundant suffixed nodes in such sub-graphs are quite common, but their elimination is a hard problem as some previous cube approaches demonstrate. MCG approach computes a data cube in two phases: First, it generates a base cuboid from a base relation with no tuples rearrangement. Second, it generates all the remaining aggregated cells, in a top-down fashion, with a unique base-MCG scan. During both MCG cube computation phases, the MCG cube size reduction method maintains the entire lattice of cuboids without common prefixed nodes and common single graph paths. During the second phase, the reduction method also eliminates common aggregated nodes that are normally frequent when sparse relations are computed. MCG performance analysis demonstrates an efficient runtime and very low memory consumption when compared to Star and MDAG full cube approaches.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".