PS-tree-based efficient boolean expression matching for high-dimensional and dense workloads
Bibliographic record
Abstract
Boolean expression matching is an important function for many applications. However, existing solutions still suffer from limitations when applied to high-dimensional and dense workloads. To overcome these limitations, in this paper, we design a data structure called PS-Tree that can efficiently index subscriptions in one dimension. By dividing predicates into disjoint predicate spaces, PS-Tree achieves high matching performance and good expressiveness. Based on PS-Tree, we first propose a Boolean expression matching algorithm PSTBloom. By efficiently filtering out a large proportion of unmatching subscriptions, PSTBloom achieves high matching performance, especially for high-dimensional workloads. PSTBloom also achieves fast index construction and a small memory footprint. Compared with state-of-the-art methods, comprehensive experiments show that PSTBloom reduces matching time, index construction time and memory usage by up to 84%, 78% and 94%, respectively. Although PSTBloom is effective for many workload distributions, dense workloads represent new challenges to PSTBloom and other algorithms. To effectively handle dense workloads, we further propose the PSTHash algorithm, which divides subscriptions into disjoint multidimensional predicate spaces. This organization prunes partially matching subscriptions efficiently. Comprehensive experiments on both synthetic and real-world datasets show that PSTHash improves the matching performance by up to 92% for dense workloads.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".