Disk-based Indexing for NIR-Trees using Polygon Overlays
Bibliographic record
Abstract
This thesis presents the NIR+-Tree, a disk-resident R-Tree variant that eliminates overlap among its minimum bounding rectangles (MBRs). The NIR+-Tree is an extension of the main-memory NIR-Tree, adopting techniques for efficient storage and retrieval on disk. By employing non-intersecting polygons instead of rectangles for data partitioning, the NIR+-Tree minimizes the number of spurious disk accesses incurred due to MBR overlap. To stabilize the height of the NIR+-Tree, the dynamically-sized polygons are stored in main-memory using an efficient encoding. Experimental results show that the NIR+-Tree is efficient at point queries and selective range queries, using 2× to 5× fewer disk accesses than its closest competitors, the R+-Tree and the R*-Tree. \n \nAdditionally, this thesis investigates bulk-loading algorithms for the NIR+-Tree. Bulk-loading can be used to efficiently construct an index from a pre-defined set of data. Bulk-loading algorithms that generate MBRs with significant overlap create NIR+-Trees with undesirable, complex polygons. This thesis shows that top-down bulk-loading algorithms are better suited for the NIR+-Tree than bottom-up algorithms, due to their overlap minimizing properties. These techniques enable the NIR+-Tree to be a complete, disk-based indexing solution for spatial data.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".