A Workload-Driven Framework for NoSQL Data Modeling and Partitioning
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
Due to the scalability problems in traditional relational database systems, a variety of NoSQL stores have emerged over the last decade to deal with big data. The lack of standard processes for designing and partitioning NoSQL datasets, as two non-orthogonal principles of distributed database systems, has led to the proposal of several recent methods. On the one hand, the existing design methods provide various conceptual modeling notations and mainly target a particular NoSQL data model that cause extra eort for designers when switching from one data model to another. Also, by providing just a set of guidelines and heuristics for the design process, many methods have to be applied manually which is an error-prone and time-consuming process. To deal with these limitations, we present a novel method for designing key-value, wide-column, and document NoSQL database schemas from the same conceptual model. It rst generates a generic NoSQL logical schema from the conceptual model and query workload of the system. Then it converts the generic schema to the schemas of targeted NoSQL data models regarding their important features and design trade-os between the read query performance and storage overhead or consistency maintenance.
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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 | 0.001 | 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