Relevance Matters: Capitalizing on Less (Top-k Matching in Publish/Subscribe)
Bibliographic record
Abstract
The efficient processing of large collections of Boolean expressions plays a central role in major data intensive applications ranging from user-centric processing and personalization to real-time data analysis. Emerging applications such as computational advertising and selective information dissemination demand determining and presenting to an end-user only the most relevant content that is both user-consumable and suitable for limited screen real estate of target devices. To retrieve the most relevant content, we present BE*-Tree, a novel indexing data structure designed for effective hierarchical top-k pattern matching, which as its by-product also reduces the operational cost of processing millions of patterns. To further reduce processing cost, BE*-Tree employs an adaptive and non-rigid space-cutting technique designed to efficiently index Boolean expressions over a high-dimensional continuous space. At the core of BE*-Tree lie two innovative ideas: (1) a bi-directional tree expansion build as a top-down (data and space clustering) and a bottom-up growths (space clustering), which together enable indexing only non-empty continuous sub-spaces, and (2) an overlap-free splitting strategy. Finally, the performance of BE*-Tree is proven through a comprehensive experimental comparison against state-of-the-art index structures 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.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".