## 1 Introduction  
The recent sharp increase in technological development and the associated growing increase in the volume of data spread and produced have led to regular data being converted into big data. As the name implies, big data indicates the type of data of large size, the forms it includes, and requiring high-speed servers for timely input [1]. Big data is the variability, size, and speed of data that needs to be achieved. This data is normally saved in large servers and is available only when necessary [2]. Big data is used to perform regular organizational processes, such as decision making and validation [3]. However, to improve efficiency, a trade-off is critical between efficiency and application size [4]. Typical examples are GPS, facial recognition cameras. The effectiveness of such sort of applications can be improved by providing large datasets for model training. Alternatively, this is not possible because large data sets need comparatively large storage space, which becomes difficult to manipulate. For this, a mechanism is needed that allows big data subgroups to carry knowledge and information similar to those found in the source data [5]. Big data also poses serious account risks that need to be processed to ensure end-user protection at the end. Accordingly, some parameters are usually indicated to ensure the quality of big data and the quality of information [6]. Examples of these parameters are: syntactical validity, appropriate attribute association, Precision, theoretical relevancy, controls, and audibility [6]. In addition, other problems are created by the management of servers, privileges, sorting, and security [7]. By 2002, the number of digital devices exceeded 92% with five Exabytes of data [8], which has been increasing since then leading the problem to develop gradually. Today, the business of big data is about $46.4 billion [8], which means that, despite problems with data processing, user interest is increasing over the years. Relative to data mining, data processing becomes very difficult with hundreds of groups classified by small differences, increased workload, and compilation time [8]. In addition to endless applications, big data has become a complex concept for data mining, information fusion, social networks, semantic network, etc. [9]. Accordingly, much attention was paid to data processing, pattern mining, data storage, analysis of user behavior, data visualization, and data tracking [10]. This devastation is intensified by the search for solutions to the problems of a large collection due to the fact that technologies such as machine learning, computational intelligence, and social networks use libraries to process data. These libraries, consecutively, increase in size as their scope expands. As a result, solutions are constantly being sought for ease of processing and scanning of big data. These solutions are data sampling, data condensation, density-based approaches, incremental learning, divide and conquer, distributed computing, and others [8]. From data handling perspectives, sampling big data has the most considerable concern of complexity, computational burden, and inefficiency for the appropriate execution of the task [11]. The effort of sampling is the number of data sets that can be added to each sample. In general, it is believed that the richness of these data sets is poor only if the sample is biased by estimation [12]. In this regard, the selection bias can be calculated and successfully determined using the reverse sampling procedure, where information is used from external resources, or using digital integration technology, where big data is combined with independent probabilistic sampling [13]. Sample size is very critical and plays a significant role in the accuracy of a system [14]. Thus, as a solution to the challenges of sampling big data, some algorithms have been introduced, such as the process of Zig Zag [15], nonprobabilistic sampling [13], inverse, and cluster sampling [16].