Bibliographic record
Abstract
Fast aggregation of data with many dimensions is a key component of many applications. The R-tree is the traditional data structure for indexing multi-dimensional data, but even the best R-tree variants suffer from performance degradation as the number of dimensions increases. The DC-tree addressed this issue by replacing Minimum Bounding Rectangle (MBR) keys with Minimum Describing Subsets (MDSs), which are less susceptible to overlap. This technique dramatically improves query performance with many dimensions, but at the cost of reduced insertion performance. Like most R-tree variants, this insertion overhead comes from expensive geometric comparisons while selecting the best child for insertion, or splitting over-full nodes. DC-trees, including the parallel PDC-tree, suffer even more from this overhead since MDSs are typically much more expensive to compare and manipulate than MBRs. This paper introduces the Hilbert PDC-tree, a parallel index structure for many-dimensional data that supports high-velocity data ingestion. This is achieved by avoiding geometric comparisons during insertion by instead inserting records based on the Hilbert index of their keys. This approach is similar to that of the Hilbert R-tree, but with special considerations for efficiently supporting many hierarchical dimensions. Additionally, a new node splitting algorithm significantly reduces overlap and improves query performance. Experiments show that the Hilbert PDC-tree scales well to a high number of dimensions, while supporting a much higher rate of ingestion and better query performance than the PDC-tree.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".