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Big Data And Political Social Networks

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Question:

Discuss about the Big Data and Political Social Networks.
 
 

Answer:

Introduction

In the present situation, a shadow has been cast on the Internet, Web and its associated entities with the explosion of data that has occurred in the last few years considering the interaction between the systems and people at multiple touch points (Anshul Sharma, 2014). This big entity which is occurring in various touch points, and as explained earlier, its general behavior is referred to as Big data. Some a while ago, Megabytes and Kilobytes used to be the utilities used in the combination of all data existing in the world. However, due to ever growing and continuous associations between systems and people, has led to rapid data growth to which new concepts such as Terabytes, Zettabytes, Gigabytes, and Petabytes have been introduced to help in computing done in the world. Researchers and theorists have propagated that Internet data would be more than the entire capacity of the living species’ brain (Rambola, 2016). The advancements in technology have been continuously taking place across all the spheres. The main reasons that have been promoting this exponential growth are the advancements made in the storage, communications, digital sensors, and in the computation, that have established massive data collection. As explained earlier, the generation of data is through varied sources which are utilized by multiple firms to understand and run different situations of business which aid them to run and understand their operations. (Rambola, 2016) explains that when the data above is examined through different methods and sources of analyzing data, it aids the businesses in their interpretation of marketing trends, studying the behaviors of customers, and taking financial and strategic decisions. When Big Data is defined, often people forget to explain that the same comprises of Datasets that are huge and which cannot be efficiently managed by the conventional systems of data management, which is denoted often by RDBMS, which is an acronym for Relational Database Management Systems (Anshul Sharma, 2014). These datasets mostly extend from Exabytes to Zettabytes.’

Big data is not really a new term (Sabitha M.S, 2015). Companies have been having huge volume of data warehouses and databases for centuries. The only distinction is how complicated it is, its size, and its growth speed. Conventional RDBMS is not enough to process the data that is big. It needs effective and efficient technology process efficiently big volume of data (Rambola, 2016). Contemporary technologies and the cloud based utilities are applied to curb the limitations of conventional RDBMS. Twitter, LinkedIn, Google, Facebook, and Amazon needed database management tools to handle the complicated and dynamic sets of data. These organizations initiated NoSQL. NoSQL are very crucial in order for the businesses to handle big datasets generated through IoT, Huge Data, big users, and cloud computing (Rambola, 2016). SQL is not used as a querying language by the NoSQL but is a database management tool in the architecture that is distributed. However, NoSQL is not another RDBMS.

The big data amount that this paper has discussed about, that is extracted from people and system interactions, which includes interactions across social networks, mobile phones, and credit cards devices, is not utilized completely and hence some resides in servers that are unknown, in a form that is unutilized and unstructured for an extended period of time. Nonetheless, in the present situation, with the Big Data evolution, the said data can be accessed and examined to generate useful information. Based on the data from Big information technology companies, quintillions od data is created in a single day. The data that is exist today in the world, 90% of it has been generated in the last two years (Rambola, 2016). The data is sourced from various sources. Some of those sources include digital videos and pictures, mobile phones and associated signals of GPS, Social media activities, transactional records of selling and purchasing, etc. In the present situation data exists anywhere and everywhere in varied formats such as texts, numbers, videos and images (Rambola, 2016). Data in itself has an exponential growth pace; however, this enormous data collection has several critical issues related with it, and problems which can be categorized as rapid data, transfer speed, security issues, and diverse data.

 

What the Big Data Means

When Big Data is spoken about; it is identified often as a catch phrase or a jargon which refers to huge volume of structured and unstructured data that consists huge datasets which are difficult to process by using database management techniques that are old (Rambola, 2016). The big size and capacity of big data has in itself the ability that can help businesses in arriving at a data driven, intelligent, and far better decisions that can help the organizations to improve their operations. In the most scenarios of the organizations, it is identified easily as either the data volume is moves to fast or is too big, or the data surpasses the current processing and storage capacity (Hakan Özköse, 2015). The insights that organizations obtain from the data is used to help the organizations achieve the revenue increase, gain competitive advantage, and establish exemplary customer retention rates. To achieve efficiently and effectively all these, the organizations should be able to capture or collect data, manipulate, format and store the data after analyzing it. Big data is a term and a term can be interpreted in different ways, through which the same concept can be defined many definitions. (James Manyika, 2011) Defines big data as the amount of data that surpasses the technological capacity to process, manage and store it efficiently. (Dazhi Chong, 2015)explains that it is a concept which defines high-volume, high-speed, high-tech, multivariate and complex data to capture, analyze, manage, store, and distribute the information. According to (Hakan Özköse, 2015), big data is high variety information assets with high velocity, high volume that needs new processing forms to facilitate the discovery of insight, decision making and optimization of processing.

Big data technologies are new generation architectures and technologies designed to obtain value from high volume multivariate sets of data effectively through discovery, high speed capturing and analyzing  (Rambola, 2016). (Ibrahim Abaker Targio Hashem, 2015)defines huge data by integrating several literature definitions as: The cluster of technologies and methodologies in which new forms are combined to expose the values that are hidden in complex, high volume, and diverse sets of data. Based on the definitions, by simply looking at the data sizes, it is enough to get an understanding oversite that traditional techniques are not effective in examining big data sets. Therefore, in order to compliment these methods, new technologies and techniques are required.

 

Real Life Applications of Big Data

In the society, business community, and in science and technology, the Big Data has several advantages. Under this section, this study discusses some of the advantages of big data.

In the modern world, this is one of the Big Data uses that is highly publicized. Big Data is utilized here to help in understanding of the customers, their preferences and behavior. Organizations are keen to extend their old data sets using browse log, data from social media, as well assessor data and text analytics (Hakan Özköse, 2015). This is in order to help the organization get the complete image of their customers. In most instances, creating predictive models is the main objective of the organizations using big data.

Big data is increasingly being used in the optimization of business processes. Based on forecasts generated from web search trends, social media and weather forecasts, retailers are able to use that information to optimize their stock. Big data analytics are also being used to improve Human Resource business processes (Anshul Sharma, 2014). This consists talent acquisition optimization and the measurement of the culture of the organization and the engagement of the staff through the application of Big data.

Research and Science is being transformed currently by the new potentials that are brought by big data (Anshul Sharma, 2014). For instance, the Swiss nuclear physics lab, CERN, the world’s most powerful and largest particle accelerator, experiments to find out how the universe was initiated and how it works. This is conducted through the analyzing of huge amounts of data generated from various sources. The data center at CERN has close to 66000 processers that are used to analyze its 30 data petabytes (Anshul Sharma, 2014). Nonetheless, it applies the power of computing of many computers that are evenly distributed across more than 149 data centers globally to examine the data. Such powers of computing can be used to transform many other research and science area.

The power of big data analytics computing enables people to decode all strings of DNA in a short period of time. This allows the medical practitioners to predict and understand the patterns of diseases and establish cures (Rambola, 2016). The future clinical experiments may not be restricted on small samples. But they may include every individual in the area of experiment target.

 

Optimizing Device and Machine Performance

Big data analytics help devices and machines become autonomous and smarter. For instance, big data tools are applied the operation on Google’s self-car driving. GPS, sensors, powerful computers, and Cameras are fitted on vehicles such as Toyota Prius to make enable them drive without human beings safely. Big Data tools are also utilized in the energy grid optimization using smart meters’ data (Rambola, 2016). Big data can even be used in the optimization of data warehouses and computer performance.

In the modern world, big data has found significant application in the High Frequency Trading (HFT) (Rambola, 2016). In this area, there is the use of algorithms of big data to make financial trading decisions. Currently, several trading of equity takes place through the use of data algorithms that is takes into account the social media signals and websites to sell and buy items in split seconds.

Big data is heavily applied in the improvement of security and in the enforcement of law. Some of the revelation is that the National Security Agency (NSA) in the United States applies the analytics of big data to stop the attack plots by the terrorists (Rambola, 2016). Other uses of big data are in the detection and prevention of cyber-attacks. In the police force, police officers use big data techniques to arrest criminals including the detection of criminal related activities. The companies specializing in the credit card integrate the application of big data analytics to detected transactions that are fraudulent.

 

Conclusion

In the present situation, a shadow has been cast on the Internet, Web and its associated entities with the explosion of data that has occurred in the last few years considering the interaction between the systems and people at multiple touch points. This big entity which is occurring in various touch points, and as explained earlier, its general behavior is referred to as Big data. Big data technologies are new generation architectures and technologies designed to obtain value from high volume multivariate sets of data effectively through discovery, high speed capturing and analyzing. In the society, business community, and in science and technology, the Big Data has several advantages. Big Data is used in optimizing and understanding business process, improving customer relation, improving public healthcare, optimizing device and machine performance, financial trading improvement, and in the law enforcement and security improvement.

 

References

Anshul Sharma, a. P. G., 2014. Analyzing Big Data. International Journal of Computer Science and Mobile Computing, 3(9), pp. 56-68.

Axel Maireder, B. E. W. H. G. d. Z. ˜. i. a. S. S., 2017. Big Data and Political Social Networks: Introducing Audience Diversity and Communication Connector Bridging Measures in Social Network Theory. Social Science Computer Review, 35(1), pp. 126-141.

Dazhi Chong, a. H. S., 2015. Big data analytics: a literature review, s.l.: s.n.

Hakan Özköse, a. E. S. A., 2015. Yesterday, Today and Tomorrow of Big Data. Procedia - Social and Behavioral Sciences, 195(1), pp. 1042-1050.

Ibrahim Abaker Targio Hashem, I. Y. N. B. A. S. M. A. G. a. S. U. K., 2015. The rise of “big data” on cloud computing: Review and open research issues. Information System, p. 98–115.

James Manyika, M. C. B. B. J. B. R. D. C. R. A. H. B., 2011. Big data: The next frontier for innovation, competition, and productivity, s.l.: McKinsey Global Institute.

Rambola, T. A. a. R. K., 2016. Literature Review On Big Data. International Journal of Advancement in Engineering Technology, Management & Applied Sciences, 3(5), pp. 21-40.

Sabitha M.S, S. a. R. S., 2015. International Journal for Research in Applied Science & Engineering Technology (IJRASET). Big Data – Literature Survey, 3(8), pp. 318-323.

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