Two ellipse-based pruning methods for group nearest neighbor queries
Why this work is in the frame
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Bibliographic record
Abstract
Group nearest neighbor (GNN) queries are a relatively new type of operations in spatial database applications. Different from a traditional kNN query which specifies a single query point only, a GNN query has multiple query points. Because of the number of query points and their arbitrary distribution in the data space, a GNN query is much more complex than a kNN query. In this paper, we propose two pruning strategies for GNN queries which take into account the distribution of query points. Our methods employ an ellipse to approximate the extent of multiple query points, and then derive a distance or minimum bounding rectangle (MBR) using that ellipse to prune intermediate nodes in a depth-first search via an R$^*$-tree. These methods are also applicable to the best-first traversal paradigm. We conduct extensive performance studies. The results show that the proposed pruning strategies are more efficient than the existing methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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