Experimental Validation as Support in the Migration from SQL Databases to NoSQL Databases
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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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".