Approximate Continuous Nearest Neighbour Query Processing in Clustered Point Sets
Bibliographic record
Abstract
In this paper we propose a strategy for continuous k-nearest neighbour query processing for location-based services. Our approach applies clustering, which has not been applied to k-nearest neighbour processing by other works. Using a clustered point set on the server, a safe region is formed using a subset of the existing clusters. As long as the user's location (i.e., query point) remains in the safe region, the data set on the server can be used to accurately answer all k-nearest neighbour queries the majority of the time. Our strategy is an approximation strategy, as there are situations where the result produced for the user may not be accurate. However, an evaluation of our strategy show that the result is accurate at least 70% of the time in most cases. We also observed that when compared to repeated k-nearest neighbour search, our strategy is computationally significantly faster for a larger dataset. Therefore, it is worth the trade-off of less than 100% accuracy to achieve results quickly, and for several applications (e.g., restaurant searching), this can be ideal.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".