Trade-off Aware Sequenced Routing Queries (or OSR Queries when POIs are not Free)
Bibliographic record
Abstract
The well-known Optimal Sequenced Routing (OSR) query considers a traveller that needs to stop by some cost-free points of interest (POIs), each belonging to a given strict sequence of categories of interest (COIs), while minimizing only the distance traveled. In this paper we extend the OSR query by adding the constraint that (1) each POI yields a non-null cost and that (2) the traveller wishes to minimize the travel distance as well as the total cost of POIs he/she stops by. We name this new query as Trade-Off Aware Sequenced Routing (TASeR). The challenging aspect of this query is that it is not always possible to optimize both travel distance and total POI cost simultaneously. As well, combining both criteria into a single one with predetermined weights may not be desirable or even feasible. As our main contribution we make use of the linear skyline paradigm, along with provably correct pruning criteria, to propose an approach that finds all optimal solutions for any linear combination of the two competing criteria very efficiently. Our experiments using real city-scale data show that our proposed approach can obtain optimal linear skyline sets in sub-second processing time for reasonably sized instances of the TASeR query. Moreover, we show that any instance of the traditional OSR query can be easily modeled as a TASeR query, hence, our proposed approach can also solve OSR queries at the expense of negligible overhead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".