Jackpine3D: A Benchmark for Evaluating 3D Spatial Database Features
Bibliographic record
Abstract
In recent years, due to the advancements in 3D data technologies, and the rise in 3D spatial applications, such as metaverse and digital twins, there has been a growing interest in efficient support of 3D spatial features in database management systems (DBMS). Processing 3D spatial queries can be significantly more complex and computationally intensive than traditional 2D spatial queries. Although a few spatial database benchmarks exist, they support 2D spatial data. Hence, there is a growing need to develop a benchmark to evaluate 3D spatial features. To address the aforementioned need, we have developed Jackpine3D, a 3D spatial database benchmark. The main goal of this benchmark is to evaluate the capability of different database systems in processing 3D spatial queries. The benchmark includes a Micro benchmark suite that consists of basic 3D spatial operations, and a Macro benchmark consisting of a few real-world spatial applications. With our benchmark, we have evaluated three database systems and compared their performance. We also identify several gaps for future researchers to address.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".