Analysis and optimization for boolean expression indexing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it