Computing closest common subexpressions for view selection problems
Bibliographic record
Abstract
Selecting a set of views for materialization is a required task in many current database and data warehousing applications including the design of a data warehouse, and the maintenance of multiple materialized views. The selected views can be materialized permanently or transiently depending on the specific view selection problem. The view selection algorithms are expensive due to the size of the search space of the problem.In this paper we propose an approach for generating candidate views for materialization for view selection problems based on the definition of the input queries. We also provide rewritings of the input queries using the generated candidate views. In generating candidate views, we do not apply costbased techniques but we try to maximize the operations in the views. Subsequently, view selection algorithms can exploit problem dependent cost functions to choose among the generated candidate views. Our approach is not restricted to a specific view selection problem. Compared to a previous one, it generates views that involve more relation occurrences (or operations) and can reduce the size of the search space which can be very large. We implement our approach and we report some experimental evaluation with comparison to previous works.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".