Bibliographic record
Abstract
Applications exist today that require the management of distributed spatial data.Since spatial data is more complex than non-spatial data, performing queries on it requires more local processing (i.e.CPU and I/O) time.Also, due to geographical distribution, data transmission costs must be considered.To reduce these costs, one can employ a distributed spatial semijoin as it eliminates unnecessary objects before their transmission to other sites and the query site.Most existing work propose different representations of the distributed spatial semijoin between two sites only, with very few works exploring its use for processing a query involving more than two sites.In this thesis, we propose both new approaches for representing the spatial semijoin in a distributed setting, and their use for processing a distributed query consisting of any number of sites.Two strategies are proposed for compactly representing the spatial semijoin that reduce both the data transmission and local processing (CPU+I/O) costs when applied in a distributed spatial query.A Global Encompassing Minimum Bounding Rectangle (GEMBR) is utilized, which is partitioned, mapped and applied in two different ways to approximate the objects in a spatial joining attribute.The first is partition indices, while the second is a bit array representation.Then each spatial semijoin is applied in a multi-site distributed spatial query processing strategy.In addition, the two-site spatial semijoin is extended to handle multiple sites so that we have a benchmark strategy for comparison purposes.We have tested the query processing algorithms for four sites, which are a part of an actual working distributed system.The algorithms are compared with respect to data transmission cost, CPU time, I/O time and false positive results.The algorithms are superior in many cases at optimizing the above criteria.The bit array representation, which is called Bloom Filter Based Spatial Semijoin (BFSJ), is evaluated with respect to different filter (Thesis) degree in Computer Science from the Department of Mathematics and Computer Science at The University of Lethbridge.I am also grateful to my committee members, Dr. Yllias Chali and Dr
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".