A Lightweight Online Framework For Query Progress Indicators
Why this work is in the frame
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Bibliographic record
Abstract
Recently there has been increasing interest in the development of progress indicators for SQL queries. In this paper we present a lightweight online framework for this problem. Our framework is online, in the sense that it refines its estimate of query progress based on feedback received during query execution. It is lightweight, since our techniques are designed to impose minimal overhead on query execution without sacrificing accuracy of estimates. Our framework can estimate progressively the output size of various relational operators and pipelines. These include binary and multiway joins as well as typical grouping operations and combinations thereof. We describe the various algorithms used to efficiently implement the estimators and present the results of a thorough evaluation of a prototype implementation of our framework in an open source data manager. Our results demonstrate the feasibility and practical utility of the approach presented herein.
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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 | 0.000 |
| Open science | 0.000 | 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