Bibliographic record
Abstract
The query processor of a relational database system executes declarative queries on relational data using query evaluation plans. The cost of the query evaluation plan depends on various statistics defined by the query and data. These statistics include intermediate and base table sizes, and data distributions on columns. In addition to being an important factor in query optimization, such statistics also influence various runtime properties of the query evaluation plan. This thesis explores the interactions between queries, data, and statistics in the query processor of a relational database system. Specifically, we consider problems where any two of the three - queries, data, and statistics - are provided, with the objective of instantiating the missing element in the triple such that the query, when executed on the data, satisfies the statistics on the associated subexpressions. We present multiple query processing problems that can be abstractly formulated in this manner. The first contribution of this thesis is a monitoring framework for collecting and estimating statistics during query execution. We apply this framework to the problems of monitoring the progress of query execution, and adaptively reoptimizing query execution plans. Our monitoring and adaptivity framework has a low overhead, while significantly reducing query execution times. This work demonstrates the feasibility and utility of overlaying statistics estimators on query evaluation plans. Our next contribution is a framework for testing the performance of a query processor by generating targeted test queries and databases. We present techniques for data-aware query generation, and query-aware data generation that satisfy test cases specifying statistical constraints. We formally analyze the hardness of the problems considered, and present systems that support best-effort semantics for targeted query and data generation. The final contribution of this thesis is a set of techniques for designing queries for business intelligence applications that specify cardinality constraints on the result. We present an interactive query refinement framework that explicitly incorporates user feedback into query design, refining queries returning too many or few answers. Each of these contributions is accompanied by a formal analysis of the problem, and a detailed experimental evaluation of an associated system.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".