Abstract
Nowadays, data are generated in a continuous streaming manner as the inputs of various applications. The sources of such generated data can be wired or wireless sensor networks commonly used in various fields of geographical, traffic, Internet of Things (IoT), financial tickers, Web2 and Web3, e-commerce, social networks, and online communities. The high volume, high variety, and high velocity of data have recently posed the challenge of 3Vs to this field, also known as the Big Data Problem. The 3Vs dimensions of complexities for the big data entails high-speed storage, scalability of database systems, suitable data models, real-time responsiveness and so on. Data model, as the representation schema of data is an essential issue since many others (e.g., DBMS systems’ design, DB languages, etc.) rely on. So, the study of data models is a key and fundamental aspect in structuring, organizing, storing, and manipulating big data. It is also the essence in various areas of cloud migration, web-scale, and so forth. In this paper, we have systematically reviewed different types of data models, the rationale behind them, their applications and support capabilities, and the technologies to switch from one model to another. To address the user needs in various fields, a systematic review method is adopted to classify and present different types and characteristics of data models.
Highlights
T HIS In previous decades, relational database management system (RDBMS) [1] was considered as the optimal solution for many data consistency and management services [2]
Regarding the applications and frequencies of reviewed data models, databases were classified under five general categories of XML and JSON, time series, relational, and NoSQL
The exponential growth of data generated from different sources, high volume and variety of data, and their rapid transfer from digital technologies brought about the growth of big data, the challenge of 3Vs, and the development of software technologies to overcome this challenge
Summary
T HIS In previous decades, relational database management system (RDBMS) [1] was considered as the optimal solution for many data consistency and management services [2]. It has emerged in a general term with unstable and flexible conceptual data models [16] and varied strategies for the databases. Among features of NoSQL databases are high scalability, availability without the need for ACID feature support [9], open-source possibility of the presented models [17], and capability of dynamic data modeling. Instead of being exploited or valued, a mass of data is given up without reaching its ultimate goal This will increase the demand for technologies, systems, tools, methods, models, structures, and concepts to receive, manage, process, and analyze big data, extract the embedded knowledge and turn it into value [21]. We examine different types of data models focusing on different representations of big data
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