Analysis and optimization for boolean expression indexing
Bibliographic record
Abstract
BE-Tree is a novel dynamic data structure designed to efficiently index Boolean expressions over a high-dimensional discrete space. BE Tree-copes with both high-dimensionality and expressiveness of Boolean expressions by introducing an effective two-phase space-cutting technique that specifically utilizes the discrete and finite domain properties of the space. Furthermore, BE-Tree employs self-adjustment policies to dynamically adapt the tree as the workload changes. Moreover, in BE-Tree, we develop two novel cache-conscious predicate evaluation techniques, namely, lazy and bitmap evaluations, that also exploit the underlying discrete and finite space to substantially reduce BE-Tree's matching time by up to 75% BE-Tree is a general index structure for matching Boolean expression which has a wide range of applications including (complex) event processing, publish/subscribe matching, emerging applications in cospaces, profile matching for targeted web advertising, and approximate string matching. Finally, the superiority of BE-Tree is proven through a comprehensive evaluation with state-of-the-art index structures designed for matching Boolean expressions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".