Bibliographic record
Abstract
As modern applications generate data at an unprecedented speed and often require the querying/analysis of data spanning a large duration, it is crucial to develop indexing techniques that cater to larger-than-memory databases, where data reside on heterogeneous storage devices (such as memory and disk), and support fast data insertion and query processing. In this paper, we propose FILM, a F ully learned I ndex for L arger-than- M emory databases. FILM is a learned tree structure that uses simple approximation models to index data spanning different storage devices. Compared with existing techniques for larger-than-memory databases, such as anti-caching, FILM allows for more efficient query processing at significantly lower main-memory overhead. FILM is also designed to effectively address one of the bottlenecks in existing methods for indexing larger-than-memory databases that is caused by data swapping between memory and disk. More specifically, updating the LRU (for Least Recently Used) structure employed by existing methods for cold data identification (determining the data to be evicted to disk when the available memory runs out) often incurs significant delay to query processing. FILM takes a drastically different approach by proposing an adaptive LRU structure and piggybacking its update onto query processing with minimal overhead. We thoroughly study the performance of FILM and its components on a variety of datasets and workloads, and the experimental results demonstrate its superiority in improving query processing performance and reducing index storage overhead (by orders of magnitudes) compared with applicable baselines.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.035 |
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".