EFFICIENT NON-INTERSECTION QUERIES ON AGGREGATED GEOMETRIC DATA
Bibliographic record
Abstract
Geometric intersection searching problems are a well-studied class of query-retrieval problems with many applications. The goal here is to preprocess a set of geometric objects so that the ones that are intersected by a query object can be reported efficiently. Often, a more general version of the problem arises, where the data comes aggregated in disjoint groups and of interest are the groups, not the individual objects, that are intersected by the query object. One approach to a generalized problem is to ignore the grouping, solve the corresponding classical problem, and then infer the groups from the reported answer. However, this is not efficient, since the number of objects intersected can be much larger than the number of groups (i.e., the output size). The problem of designing efficient, output-sensitive query algorithms for generalized intersection searching has received much attention in recent years, and such solutions have been developed for several problems. This paper considers a new class of generalized query-retrieval problems. Specifically, given aggregated geometric data the goal is to report the distinct groups such that no objects from those groups are intersected by the query. Of interest in these generalized non-intersection searching problems are solutions where the query time is sensitive to the output size, i.e., the number of groups reported. Unfortunately, the obvious approaches of (i) solving the corresponding generalized intersection searching problem and reporting the complement, or (ii) solving a generalized intersection searching problem with the complement of the query are either inefficient or incorrect. This paper provides efficient, output-sensitive solutions to several generalized non-intersection searching problems, using techniques such as geometric duality, sparsification, persistence, filtering search, and pruning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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 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".