Nowadays, data with several characteristics such as volume, variety etc. are generated daily, i.e. big data; its complexity cannot be overemphasized. On the other hand, schema free NoSQL databases keep emerging at almost the same phase to accommodate such data which cannot be efficiently managed by relational databases. However, this advancement brings about the challenge to model such flexible databases and capably manage big data despite its complexity. In doing that, developers tend to apply their relational modeling skills; nonetheless, such skills may not be directly compatible with NoSQL databases due to their schema flexibility, linear scalability among others. To alleviate this difficulty, we propose a standard for modeling NoSQL databases, document-stores in particular. The standard can be classified as i) cardinality notations, and ii) relationship modeling styles. With such standard, NoSQL document-store databases can be properly designed, automated database testing can be applied, and database performance and stability can be considerably improved. To achieve this, experimental method is applied. Also, exploratory approach was used to explore the available literature as well as experts consultations. All possible entity relationships were extracted, aggregated and compiled from a heuristic evaluation of existing 4 different document-store databases. An experiment was conducted to assess the effect of the proposed standards, results indicate a profound improvements in various aspect of document modeling when the proposed standards are adopted, especially in a large scaled databases.

NoSQL databases provide high availability and efficiency in data processing but at the expense of weaker consistency. In this paper, we propose a new approach in order to test NoSQL key/value databases in general and their CRUD operations in particular. We design a new context-aware model that takes into account the contextual requirements of clients (users) and the NoSQL database system. Accordingly, we develop a transaction model and testing criteria in order to test NoSQL databases by taking into account transactional and nontransactional CRUD operations. Results from testing criteria are used to analyse the trade-off between availability and consistency of NoSQL databases. In addition, these are used to help NoSQL database users and developers to choose between transactional and non-transactional CRUD operations.

New generation databases also called NoSQL (Not only SQL) databases are highly scalable, flexible, and low-latent. These types of databases emerge as a result of the rigidity shown by traditional databases to handle today's data which is voluminous, highly diversified and generated at a very high rate. With NoSQL, problems such as database expansion difficulties, low query performance and low storage capacity are addressed. However, the inherent complexity of contemporary datasets coupled with programmers' low NoSQL modeling competence are increasingly making database modeling and design vastly challenging, especially when parameters like consistency, availability and scalability are to be balanced in accordance with system requirements. As such, a schema suggestion model for NoSQL databases is posed to address this balancing issue. The proposed model aims to abstractly suggest schemas at the initial stage of system development based on user defined system requirements and CRUD (Create, Read, Update and Delete) operations among others. This is achieved through the adaptation of exploratory and experimental approaches of research. Also, few mathematical formulas are introduced to calculate clusters availability during entity mappings. A comparison was conducted between the schema produced using the proposed model and the one without. Results obtained shows substantial improvement in the areas of security and read-write query performance.

NoSQL databases are capable of storing and processing big data which is characterized by various properties such as volume, variety and velocity. Such databases are used in a variety of user applications that need large volume of data which is highly available and efficiently accessible. But they do not enforce or require strong data consistency nor do they support transactions. This paper investigates into the transaction processing in consistency-aware applications hosted on MongoDB and Riak which are two representatives of Document and Key-Value NoSQL databases, respectively. It develops new transaction schemes in order to provide NoSQL databases with transactional facilities as well as to analyze the effects of transactions on data consistency and efficiency in user’s applications. The proposed schemes are evaluated using the YCSB + T benchmark which is based on Yahoo! Cloud Services Benchmark (YCSB). Experimental results show that using the proposed schemes, strong consistency can be achieved in MongoDB and Riak without severely affecting their efficiency. We also conduct experiments in order to analyse the level of consistencyof MongoDB and Riak transactional systems.

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.