Mining thick skylines over large databases
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. People recently are interested in a new operator, called skyline [3], which returns the objects that are not dominated by any other objects with regard to certain measures in a multi-dimensional space. Recent work on the skyline operator [3, 15, 8, 13, 2] focuses on efficient computation of skylines in large databases. However, such work gives users only thin skylines, i.e., single objects, which may not be desirable in some real applications. In this paper, we propose a novel concept, called thick skyline, which recommends not only skyline objects but also their nearby neighbors within ε-distance. Efficient computation methods are developed including (1) two efficient algorithms, Sampling-and-Pruning and Indexing-and-Estimating, to find such thick skyline with the help of statistics or indexes in large databases, and (2) a highly efficient Microcluster-based algorithm for mining thick skyline. The Microclusterbased method not only leads to substantial savings in computation but also provides a concise representation of the thick skyline in the case of high cardinalities. Our experimental performance study shows that the proposed methods are both efficient and effective. 1
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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 | 0.001 | 0.001 |
| 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