Optimal search algorithm in a big database using interpolation–extrapolation method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fast data search is an important element of big data in the modern era of internet of things, cloud computing, and social networks. Search using traditional binary‐search algorithm can be accelerated by employing an interpolation search technique when the data is regularly distributed. In this work, the interpolation search is investigated in which the search results provided unexpected sluggish progress during a search in a large database due to the irregular distribution of data. Irregular distribution of data does not allow the interpolation to make a good prediction about the location of the search item. To overcome this issue, an interpolation–extrapolation search (IES) method is proposed where the interpolation method is integrated with an extrapolation method that balances the lower and upper bounds of the search interval. The proposed method provides faster convergence property than the binary search and the interpolation method. Hence, the proposed IES method provides a faster search for items in a big database.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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