Maximizing Bigdata Retrieval: Block as a Value for NoSQL over SQL
Bibliographic record
Abstract
This paper presents NoSQL Over SQL Block as a Value Database (NOSD), a system that speeds up data retrieval time and availability in very large relational databases. NOSD proposes a Block as a Value model (BaaV). Unlike a relational database model where a relation is$R(K,\ A_{1},\ A_{2},\ \ldots A_{n})$, with a key attribute$K$and a set of attributes of the relation:$A_{1}, A_{2}, \ldots A_{n}$, BaaV represents a relation$R(K, r_{1}, r_{2}, \ldots r_{n})$with a key attribute$K$and a set of$n$relations called blocks. Each$r$contains a set of its own attributes denoted as$r(k,\ a_{1},\ a_{2},\ldots a_{n})$with a key attribute$k$and a set of$n$attributes. The relations$r_{1}, r_{2}, \ldots r_{n}$in$R$are related through foreign key relationships to a super relation$R$with primary key$K$. The BaaV model is then denoted in a keyed block format$R\{K,\ B\}$, where$K$is a key to a block of values$B$of partial relations implemented on NoSQL databases and replicating existing large relational database systems. As opposed to conventional systems such as Zidian, Google's Spanner, SparkSQL and Simple Buttom-Up (SBU) which implement SQL over NoSQL and replicate data into different nodes, NOSD implements NoSQL over SQL and uses Lucene functionality on NoSQL to enhance data retrieval costs. Experimenting with our proposed model, we demonstrated the performance of NOSD under the following conditions to prove its novelty (a) scan free queries, and (b) bounded queries on NoSQL databases. We showed that NOSD (a) performs excellently than ordinary relational databases (b) guarantees no scans for no scan queries (c) allows parallelization in query execution, and (d) can be deployed into existing SQL databases with guaranteed horizontal scalability, data retention and accurate autonomous data replication. Using existing benchmark systems, we demonstrated that NOSD outperforms existing SQL databases, SQL over NoSQL systems and is novel in ensuring that existing large SQL database systems utilize the functionalities of NoSQL databases without data loss.$A_{1}, A_{2}, \ldots A_{n}$
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".