Efficient Parallel Boolean Expression Matching
Bibliographic record
Abstract
Boolean expression matching plays an important role in many applications. However, existing solutions still show efficiency and scalability limitations. For example, existing solutions often exhibit degraded performance when applied to high-dimensional and diverse workloads, and existing algorithms rarely consider supporting concurrent matching and index updating under multicore environments. To overcome these limitations, in this article, we first design the PS-Tree data structure to efficiently index Boolean expressions in one dimension. By dividing predicates into disjoint predicate spaces, PS-Tree achieves high matching performance and good expressiveness. Based on the PS-Tree , we propose a Boolean expression matching algorithm called PSTDynamic . By dynamically adjusting the index and efficiently filtering out a large proportion of unmatching expressions, PSTDynamic achieves high matching performance under high-dimensional and diverse workloads. For multicore environment, we further extend the PSTDynamic algorithm to PSTParallel to achieve scalability with lower matching latency and higher matching throughput. We run experiments on both synthetic and real-world datasets. The experiments verify that our proposed algorithms show high efficiency and parallelism. Moreover, they also achieve fast index construction and a small memory footprint. Comprehensive experiments show that our solutions drastically outperform state-of-the-art methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".