Efficient top-k query answering using cached views
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
Top-k query processing has recently received a significant amount of attention due to its wide application in information retrieval, multimedia search and recommendation generation. In this work, we consider the problem of how to efficiently answer a top-k query by using previously cached query results. While there has been some previous work on this problem, existing algorithms suffer from either limited scope or lack of scalability. In this paper, we propose two novel algorithms for handling this problem. The first algorithm LPTA+ provides significantly improved efficiency compared to the state-of-the-art LPTA algorithm [26] by reducing the number of expensive linear programming problems that need to be solved. The second algorithm we propose leverages a standard space partition-based index structure in order to avoid many of the drawbacks of LPTA-based algorithms, thereby further improving the efficiency of query processing. Through extensive experiments on various datasets, we demonstrate that our algorithms significantly outperform the state of the art.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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