Extending q-grams to estimate selectivity of string matching with low edit distance
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Bibliographic record
Abstract
There are many emerging database applications that require accurate selectivity estimation of approximate string matching queries. Edit distance is one of the most commonly used string similarity measures. In this paper, we study the problem of estimating selectivity of string matching with low edit distance. Our framework is based on extending q-grams with wildcards. Based on the concepts of replacement semilattice, string hierarchy and a combinatorial analysis, we develop the formulas for selectivity estimation and provide the algorithm BasicEQ. We next develop the algorithm OptEQ by enhancing BasicEQ with two novel improvements. Finally we show a comprehensive set of experiments using three benchmarks comparing OptEQ with the stateof-the-art method SEP IA. Our experimental results show that OptEQ delivers more accurate selectivity estimations. 1.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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