Efficient processing of distance–time <i>k</i> th‐order skyline queries in bicriteria networks
Bibliographic record
Abstract
With the increasing complexity of traffic conditions in road networks, the nearest destination cannot be necessarily reached in the fastest time. The traditional nearest neighbor (NN) and k NN searches in spatial network databases with single cost criterion are often strongly restrictive. In this paper, the authors consider the problem of k th‐order skyline queries in bicriteria networks, where edges represent road segments. Their proposed k th‐order skyline queries consider distance, time preferences of each edge, thus having two kinds of skyline queries, named distance optimal k th‐order skyline query (DO‐ k OSQ) and time optimal k th‐OSQ (TO‐ k OSQ). They design algorithms for the two kinds of skyline queries in bicriteria networks based on incremental network expansion method and further develop a maximum distance/time restriction strategy to improve the efficiency of the algorithms. Experimental results show that all of their methods are far below 1000 input–output input/output (IO) accesses and 1 s of central processing unit (CPU) time. For real road networks, their k OSQ+ queries need only 51.6% IO accesses and 59.6% CPU time of those for k OSQ queries, whereas for the larger road network the percentages are 51.8% and 51.2%, respectively. Thus, the results indicate the efficiency and effectiveness of their proposed methods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".