Query Sampling in DB2 Universal Database Jarek Gryz Junjie GuoYork University andCenter for Advanced Studies IBM Toronto Lab{jarek,jguo}@cs.yorku.ca
Bibliographic record
Abstract
ABSTRACT Executing ad hoc queries against large databases can be prohibitively expensive. Exploratory analysis of data may not require exact answers to queries, however: results based on sampling the data are often satisfactory. Supporting sampling as a primitive SQL operator turns out to be difficult because sampling does not commute with many SQL operators. In this paper, we describe an implementation in IBM Rfl DB2 Rfl Universal Database (UDB) of a sampling operator that commutes with some SQL operators. As a result, the query with the sampling operator always returns a random sample of the answers and in many cases runs faster than it would have without such an operator. 1. INTRODUCTION Executing ad hoc queries against large data warehouses can be prohibitively expensive. The exploratory analysis of data may not require exact answers to queries, however: results based on sampling the data are often satisfactory. Effective techniques already exist for aggregation based on sampling [1, 10, 12, 21]. However, supporting sampling as a primitive SQL operator turns out to be difficult. If sampling is to improve query performance, it should be applied to the base tables rather than to the result of the query. The problem is that sampling (or, more specifically, random sampling) does not commute with many SQL operators so it cannot be, in general, pushed down the query tree.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".