With data increasing globally, the term “Big Data” is mainly used to describe large datasets. Compared with other traditional databases, Big Data includes a large amount of unstructured data that must be analyzed in real time. Big Data also brings new opportunities for the discovery of new values that are temporarily hidden . Big Data is a broad and abstract concept that is receiving great recognition and is being highlighted both in academics and business. It is a tool to support the decision-making, process by using technology to rapidly analyze large amounts of data of different types (e.g., structured data from relational databases and unstructured data such as images, videos, emails, transaction data, and social media interactions) from a variety of sources to produce a stream of actionable knowledge . After the data is collected and stored, the biggest challenge is not just about managing it but also the analysis and extraction of information with significant value for the organization. Big Data works in the presence of unstructured data and techniques of data analysis that are structured to solve the problem . A combination called the 4Vs characterizes Big Data in the literature: volume, velocity, variety, and value . Volume has a great influence when describing Big Data as large amounts of data are generated by individuals, groups, and organizations.
The second item, velocity, refers to the rates at which Big Data are collected, processed, and prepared—a huge, steady stream of data that is impossible to process with traditional solutions, for this reason, it is important to consider not only “where” data are stored but also “how” they are stored.
The third item, variety, is related to the types of data generated from social sources, including mobile and traditional data. With the explosion of social networks, smart devices, and sensors, data have become complex because they include semi-structured and unstructured information from log files, web pages, index searches, cross media, e-mail, documents, and forums.
Finally, the value can be discovered from the analysis of the hidden data, so Big Data can provide new findings of new values and opportunities to assist in making decisions.
However, management of this data can be considered as a challenge for organizations . In order to demonstrate the differentiation between Big Data and Small Data, we analyzed them using five main characteristics: data sources, volume, velocity, variety, and value, in Table 1. Importantly, relational databases are not obsolete, on the contrary, they continue to be useful to a number of applications. In practice, how larger a database becomes, the higher the cost of processing and labor, so it is necessary to optimize and add new solutions to improve storage providing greater flexibility. For the purpose to better understand the impact of science and Big Data solutions, the applications and Big Data solutions in the following different contexts will be presented: education, social media and social networking, and smart cities. Grillenberger and Fau used educational data to analyze student performance . Their learning styles were also clarified by the use of Big Data in conjunction with teaching strategies to gain a better understanding of the students’ knowledge and an assessment of their progress. These data can also help identify groups of students with similar learning styles or their difficulties, thus defining a new form of personalized learning resources based on and supported by computational models. Big Data has created new opportunities for researchers to achieve high relevance when working in social networks. In this context, Chang, Kauffman and Kwon used communications environments to discuss the causes of the paradigm shift and explored the ways that decision support is researched, and, more broadly, applied to the social sciences . In the context of a smart city, Dobre and Xhafa provide a platform for process automation collection and aggregation of large-scale information. Moreover, they present an application for an intelligent transportation system . The application is designed to assist users and cities to resolving the traffic problems in big cities. The combination of these services provides support for the application in intelligent cities that can, benefit from using the information dataset. The value of Big Data is driving the creation of new tools and systems to facilitate intelligence in consumer behavior, economic forecasting, and capital markets. Market domination may be driven by which companies absorb and use the best data the fastest. Understanding the social context of individuals’ and organizations’ actions means a company can track not only what their customers do but also get much closer to learning why they do what they do.
To date, for the use of Big Data, a modern infrastructure is needed to overcome the limitations related to language and methodology. Guidelines are needed in a