Abstract
The way a database schema is designed has a high impact on its performance in relational databases, which are symmetric in nature. While the problem of schema optimization is even more significant for NoSQL (“Not only SQL”) databases, existing modeling tools for relational databases are inadequate for this asymmetric setting. As a result, NoSQL modelers rely on rules of thumb to model schemas that require a high level of competence. Several studies have been conducted to address this problem; however, they are either proprietary, symmetrical, relationally dependent or post-design assessment tools. In this study, a Dynamic Schema Proposition (DSP) model for NoSQL databases is proposed to handle the asymmetric nature of today’s data. This model aims to facilitate database design and improve its performance in relation to data availability. To achieve this, data modeling styles were aggregated and classified. Existing cardinality notations were empirically extended using synthetically generated queries. A binary integer formulation was used to guide the mapping of asymmetric entities from the application’s conceptual data model to a database schema. An experiment was conducted to evaluate the impact of the DSP model on NoSQL schema production and its performance. A profound improvement was observed in read/write query performance and schema production complexities. In this regard, DSP has significant potential to produce schemas that are capable of handling big data efficiently.
Highlights
With the rise of asymmetric data, there emerge new concerns about how data can be managed efficiently and effectively in a conventionally symmetric environment [1,2]
In order to evaluate the impact of the Dynamic Schema Proposition (DSP) model, a query execution runtime of its schema was compared against the schemas produced by industry experts who hold a minimum of nine years working experience
Schema Proposition (DSP) model for Not only SQL (NoSQL) databases is proposed in this study
Summary
With the rise of asymmetric data, there emerge new concerns about how data can be managed efficiently and effectively in a conventionally symmetric environment [1,2]. One of these concerns is the storage capability of the relational databases. Relational databases provide engines that provide central control of redundancy and data access patterns, enforce a schema, and eliminate inconsistencies [8]. This has led to the invention of a more flexible database called. These types of databases do not require full knowledge of the target data at the initial stage of database design, because they are Symmetry 2020, 12, 1799; doi:10.3390/sym12111799 www.mdpi.com/journal/symmetry
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