On dominating your neighborhood profitably
Bibliographic record
Abstract
Recent research on skyline queries has attracted much interest in the database and data mining community. Given a database, an object belongs to the skyline if it cannot be dominated with respect to the given attributes by any other database object. Current methods have only considered so-called min/max attributes like price and quality which a user wants to minimize or maximize. However, objects can also have spatial attributes like x, y coordinates which can be used to represent relevant constraints on the query results. In this paper, we introduce novel skyline query types taking into account not only min/max attributes but also spatial attributes and the relationships between these different attribute types. Such queries support a micro-economic approach to decision making, considering not only the quality but also the cost of solutions. We investigate two alternative approaches for efficient query processing, a symmetrical one based on off-the-shelf index structures, and an asymmetrical one based on index structures with special purpose extensions. Our experimental evaluation using a real dataset and various synthetic datasets demonstrates that the new query types are indeed meaningful and the proposed algorithms are efficient and scalable. 1.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".