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
Database statistics are crucial to cost-based optimizers for estimating the execution cost of a query plan. Using traditional basic statistics on base tables requires adopting unrealistic assumptions to estimate the cardinalities of intermediate results, which usually causes large estimation errors that can be several orders of magnitude. Modern commercial database systems support statistical or sample views, which give more accurate statistics on intermediate results and query sub-expressions. While previous research focused on creating and maintaining these advanced statistics, only little effort has been done towards automatically recommending the most beneficial statistical views to construct. In this paper, we present StatAdvisor , a system for recommending statistical views for a given SQL workload. The StatAdvisor addresses the special characteristics of statistical views with respect to view matching and benefit estimation, and introduces a novel plan-based candidate enumeration method, and a benefit-based analysis to determine the most useful statistical views. We present the basic concepts, architecture, and key features of StatAdvisor , and demonstrate its validity and benefits through an extensive experimental study using a prototype that we built in the IBM® DB2® database system as part of the DB2 Design Advisor tools.
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 | 0.001 |
| 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