The price of generality in spatial indexing
Bibliographic record
Abstract
Efficient indexing can significantly speed up the processing of large volumes of spatial data in many BigData applications. Many new emerging spatial applications (e.g., biomedical imaging, genome analysis, etc.) have varying indexing requirements, thus, a unified indexing infrastructure for implementing new indexing schemes without requiring knowledge of database internals is beneficial. However, designing a generic indexing framework is a challenging task. We study the issues with general indexing schemes, such as the GiST (used in PostGIS) and expose the tradeoff between generality and performance, showing that generality can be severely detrimental to performance if the abstractions are not carefully designed. Our experiments indicate that the GiST framework, as implemented in PostgreSQL/PostGIS, performs 4.5-6x slower for filtering records through the index, compared to a custom R-tree implementation. We also isolate the GiST-specific overhead by implementing the framework outside the DBMS, showing that the GiST-based R-tree is up to 2x slower than the raw R-tree algorithm that it uses internally. We conclude that although a generic framework for a wide range of spatial BigData application domains is desirable, implementers of new frameworks need to be careful in designing the abstractions to avoid paying a hefty performance penalty.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".