Using Spatial Semijoins Over Multiple Sites in Distributed Spatial Query Processing
Bibliographic record
Abstract
We present a strategy for geographically distributed spatial query optimization that involves multiple sites. Previous work in the area of geographically distributed spatial query processing and optimization focused only on strategies for performing spatial joins and spatial semijoins, and geographically distributed spatial queries that only involve two sites. We propose a strategy for optimizing a geographically distributed spatial query, which uses spatial semijoins and can involve any number of sites in a geographically distributed spatial database. It identifies and initiates spatial semijoins from the smaller spatial relations in order to reduce the larger spatial relations. By doing so, the data transmission and I/O costs are significantly reduced. We compare the performance of our strategy against the naïve approach of shipping entire spatial relations to the query site. We find that our optimized strategy minimizes the data transmission cost and I/O cost in all cases, and significantly in specific situations. In addition, the CPU cost is not significantly affected by our strategy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".