Big Data
Big Data is a massive set of data that drives companies, enterprises, and businesses, comprising huge volumes of information generated from transactions, sales figures, and stakeholder interactions. However, some companies face particular challenges with Big Data. These may be data quality, data storage, validation, and data security. With the increase in the volume of Big Data, data security has become the most complex aspect of data analysis (Karthiban and Raj, 2019, p. 129).
For example, the Internet of Everything can be considered a massive set of information, as it relies on data and processes connected to the Internet (Karthiban and Raj, 2019, p. 132). In this case, data analysis is an essential part of processing such a massive amount of data. Data analysis is made continuously, and consequently, this requires active devices connected to the Internet. Moreover, most of this data is usually stored in cloud memory, making the system vulnerable to attacks and data leakage.
Data Mining Methods
Data mining is the process of predicting outcomes by identifying anomalies, patterns, and correlations in large datasets. This process is typically used to enhance sales, strengthen customer relationships, mitigate risks, and reduce expenses. However, data mining without notification can pose a challenge to data safety (Karthiban and Raj, 2019, p. 131).
Information gathered by the Internet of Everything is found to be extremely helpful and essential data. Hence, data mining undoubtedly plays a crucial role in automating systems, such as the Internet of Everything, to deliver more comfortable services and surroundings. With the development of technologies, the Internet of Everything needs to operate in real-time conditions. Thus, data mining methods are being implemented using virtual machines.
Types of Visualization
Big Data visualization methods include interactive content, such as maps, charts, infographics, and plots. These techniques are used to gain a deeper understanding of the company’s processes. Furthermore, data visualization often helps overcome specific safety issues. For example, big data is typically categorized into three groups: structured, semi-structured, and unstructured (Karthiban and Raj, 2019, p. 131). These methods are analytical tools for data collection and flexible storage that improve the data architecture.
In the case of the Internet of Everything, data-driven techniques could be employed (Karthiban and Raj, 2019, p. 131). These may include authentication, unstructured distribution, tracing, anonymization, and encryption. These methods are achieved by identifying the data processing steps and representing the data graphically.
Application of Big Data Techniques to a Problem
Data analytics is crucial in processing massive data sets, including those from the Internet of Everything. The number of devices connected to the system is expected to reach one hundred billion by the end of 2020 (Karthiban and Raj, 2019, p. 133). Regarding this amount of information, several concerns related to the security of the data may arise.
Thus, the privacy of the information must be the primary priority when processing such a large amount of data. Many data processing technologies have started using encryption as a security tool to decrease the consequences of data leaks. The fact that data is encrypted adds a degree of protection, especially when contrasted with the clients’ personal information.
Reference List
Karthiban, K. and Raj, J. S. (2019) ‘Big data analytics for developing secure Internet of everything’, Journal of ISMAC, 1(2), pp. 129-136. Web.