Experimental Validation as Support in the Migration from SQL Databases to NoSQL Databases
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
NoSQL databases, also known Not only SQL databases, are a new type of databases that provides structures other than the tabular relations used in relational databases, for storage and retrieval data. This new databases are now a valuable asset to design complex real-time applications that use Big Data in cloud environments (NoSQL cloud databases). Today, the migration process from relational databases to NoSQL databases is unclear and mainly based on heuristic approaches such as the developers’ experience or intuitive judgments. This paper, which forms part of a more extensive research project regarding how the design and use of a guidelines set could improve the migration process. The results present an experiment designed to obtain a baseline that allows an effective comparison between two migration processes: the first one, without the use of any guidelines and based on the traditional heuristic approach and the second one, with the guidelines. The experiment reports that the use of such guidelines improves the migration process. KeywordsColumn oriented databases, NoSQL databases, distributed databases, software experimentation, cloud computing.
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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 | 0.004 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it