UniqueNOSD: a novel framework for NoSQL over SQL databases
Bibliographic record
Abstract
To date, most large corporations still have their core solutions on relational databases but only use non-relational (i.e. NoSQL) database management systems (DBMS) for their non-core systems that favour availability and scalability through partitioning while trading off consistency. NoSQL systems are built based on the CAP (i.e., Consistency, Availability and Partitioning) database theorem, which trades off one of these features while maintaining the others. The need for systems availability and scalability drives the use of NoSQL, while the lack of consistency and robust query engines as obtainable in relational databases, impede their usage. To mitigate these drawbacks, researchers and companies like Amazon, Google, and Facebook run ’SQL over NoSQL’ systems such as Dynamo, Google’s Spanner, Memcache, Zidian, Apache Hive and SparkSQL. These systems create a query engine layer over NoSQL systems but suffer from data redundancy and lack consistency obtainable in relational DBMS. Also, their query engine is not relational complete because they cannot process all relational algebra-based queries as obtainable in a relational database. In this paper, we present a ’Unique NoSQL over SQL Database’ (UniqueNOSD) system, an extension of NOSD and an inverse of existing approaches. This approach is motivated by the need for existing systems to fully deploy NoSQL data store functionalities without the limitation of building an extra SQL layer for querying. To allow appropriate storage and retrieval of data on document-based NoSQL databases without data redundancy and inconsistency while encouraging both horizontal and vertical partitioning, we propose NoSQL over SQL Block as a Value ( $$\text {BaaV}$$ ) data storage strategy. Unlike relational database model where a relation is represented as $$R(k, A_1, A_2,\dotsc , A_n)$$ , with a key attribute $$k = k_1, \dotsc , k_n$$ and $$k_i$$ is the primary key to the set of attributes $$A_i, i= 1,2,\dotsc ,n$$ of the relation, in $$\text {BaaV}$$ (represented as a tuple (K, B) where K means key and B means block). We represent a relation as $$R(K,r_1,r_2,\dotsc ,r_n)$$ with a key attribute K and a set of n relations (i.e., r) called blocks B and each r $$\in B$$ contains a set of its own attributes and is denoted as $$r(k, A_1, A_2,\dotsc , A_n)$$ with a key attribute k and a set of n attributes typical to a relational model. The relations $$r_1, r_2,\dotsc ,r_n$$ in R of $$\text {BaaV}$$ are related through foreign key relationships. Using existing benchmark systems of ’SQL over NoSQL’, relational databases and real-life datasets for our experiments, we demonstrated that our NoSQL over SQL system outperforms existing relational databases, SQL over NoSQL systems and is novel in ensuring data consistency, scalability, query execution and improving data storage and retrieval in large database systems without data loss and enhancing improved performance on NoSQL database.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".