IT for Management for Cloud Storage Capacities - myassignmenthelp

Question: Discuss about theIT for Management for Cloud Storage Capacities. Answer: Introduction The report reflects literature review on big data analytics. The literature review is a manuscript of academic papers which include the recent knowledge related to practical findings contribution to the specific topic. Literature Review In the world of information era, there is vast data that have become accessible on hands of decision makers. Big data refers to as the database which is big and high in terms of velocity as well as in variety that create challenge to handle because of traditional techniques and tools. This big data contributes effectively while making rapid decision. The companies make use of big data after knowing the fact that there is change in data for regular dealings. According to the Gandomi and Haider, 2015, big data analytics is where the updated analytic techniques are implemented on large set of data. The analytics that is founded on the large data sample discloses and leverages the change in business. Though, author confesses that it is difficult to manage the large set of data. Another Bollier and Firestone, 2010, shared the views for big data analytics that permits organization to examine a mix of structured, unstructured and semi-structured data in the examination of appreciated busine ss information. The big data is used by many companies and business so that they can make the right decision for their organization. Cloud storage capacities According to Wang, et.al, 2010, cloud storage refers to as the model of data storage that is used for storing the digital data in logical pools. In addition, cloud storage is the capacities that are required to the store particular data. Moreover, the author said that cloud computing and cloud storage are two different terms. Contradicting this, another Krutz and Vines, 2010 said cloud storage is cloud computing in the terms of IT. Cloud storage is basically storing the data and filed and carrying out the backups to an outside location offsite. On the other hand, cloud computing is running the application with the help of a cybernetic desktop over a protected internet connection. In addition, the researcher reflected that big data and cloud computing are adjoined with each other. The big data offers operators the facility to make use of commodity computing to practice dispersed queries through numerous datasets and return resultant sets in an appropriate method. Moreover, with the he lp of cloud and web, the large data sources store distributed fault-tolerated databases and then gets processes by programming model for a big database along with a similar dispersed algorithm in a cluster. The author also reflected that big data make use of cloud storage instead of local storage system that is attached to electronic data or computer because the fact is that there is need of the large cloud storage capacity to store the data. Another Agrawal, Das and El Abbadi, 2010, reflected that evaluation of the big data is only determined by advanced cloud-based applications that are established with the usage of virtualized technologies. Big data make use of the cloud computing that offers them the facilities for the computation, processing and serves as a service model. In addition, the author indicates that the cloud communication infrastructure can offer an effective display place to perform the big data analysis to address the data storage. Along with the author, said that there are numerous cloud-based technologies that need to manage up with the innovative environment because the trading with big data for the parallel processing has become progressively difficult. For instance; MapReduce is one of the moral examples of big data handling in a cloud environment because it allows for processing of large capacity of datasets that is stored in parallel in cluster. Neural networks Neural networks are another important aspect of the big data analytics. This is a computer system showed on the human brain and the nervous system. The neural networks are commonly known as an artificial neural network in the terms of IT which means a variety of deep learning technologies. According to Chen, Chiang, and Storey, 2012, a neural network is a system of software and hardware patterned after the processes of the neurons in the human brain in the terms of information technology. Another Najafabadi, et.al, 2015, shared his view, according to him, artificial neural networks natural metaphor illustration is a brain of an individual. The author reflected the use of neural network by big data. Today, in the developing technology century there is the development of computation power which will make the use of the network by big data in order to achieve the success mainly in application of big data for example; audio big data analysis, medical big data analysis and visual big dat a analysis. Another George, Haas, and Pentland, 2014, agreed to the same and said that neural networks are leading from the point of view of artificial-intelligence researchers. For instance, the AlphaGo beating human champion in GO game attracted even more public interest. Therefore, this reflects that big data + neural networks are becoming one of the driving forces that lead to the innovation, living development, and social promotion. Moreover, the examples clearly reflect that big data and neural networks match perfectly. Along with this, the basic concepts and technology that is used in the big data also reflect that both big data and neural networks are mutually reinforcing. Moreover, big data is capable enough to extract the abstract features from the raw data. This is fact that both big data and neural networks can combine the multiple information sources, capture changes in the data and process the heterogeneous data. On the other hand, large volume in big data permits the tremendous training samples that provide training related to neural networks with a large number of parameters. Though, this is true that there are some problems in big data and neural networks pattern. These problems are associated with the big data such as how to ensure the consistency in high-dimensional sparse space, how to implement of the knowledge-only storage, how to depict temporal correlation and prediction related to big data. On the other hand, in neural networks, there is need of structure for further research and investigation for development because there is lack of the theoretical guidelines which leads to an inherent problem. Artificial intelligence An artificial intelligence is the capability of a digital processor of computer-controlled robot to process the task that is associated with intelligent begins. This term is particularly applied in the developing system that endowed with intellectual processes characteristic of human. Russell and Norvig, 2016 states, it is a part of computer science that focuses on the formation of intelligent machineries that act and work identical like a human. In addition, intelligence is described as the ability to observe the information and to retain it as knowledge to be applied towards the adaptive behaviors within the context. This is fact that world is growing rapidly and size of data collected across the globe is increasing. The data is becoming more contextually relevant and meaningful which breaks the new ground for artificial intelligence. According to Fligler, et.al, 2010, there are many big companies that have the straight contact to sources of data that can feed the artificial processes to identify patterns and realize the behaviors. There is no longer reliant on the subdivisions of the data to conduct the analysis as large companies associate big data with artificial intelligence to create a wide range of professional advantages from the real-time customer credit support to the new product offers. According to the Nilsson, 2014, artificial intelligence and big data analytics are two most promising technology paths that the business make use in near future to make intelligence decisions linked to the past knowledge of the businesses. Though, it is difficult to understand convergence and inter-dependence of both the technologies in the real world which leads to success. For instance, the big data analysis at retail giant Walmart provides them facility to make the automatic decisions. The company has approx. 245 million customers visiting 10,900 stores with 10 websites across the world (Anuradha, 2018). The AI processes in the Walmart Company helped them in making the self-governing decision like automatic placing of an order with suppliers after considering the demand data from customers and details for number of units of each stock of their products that is required to be held in each stores. This reflects that artificial intelligence is all about providing the assistance to co mputer to learn a thing or two from that data. Conclusion In the end, it can be concluded that there is an effective use of the big data in different fields. Moreover, big data get a link to the different IT terms which include cloud storage capacities, neural networks, and the artificial intelligence. References Agrawal, D., Das, S. and El Abbadi, A. (2010) Big data and cloud computing: new wine or just new bottles?.Proceedings of the VLDB Endowment,3(1-2), pp.1647-1648. Anuradha, C. (2018) How is Big Data empowering Artificial Intelligence: 5 essentials you need to know [Online]. Available on https://yourstory.com/2018/02/big-data-empowered-artificial-intelligence/ [Accessed 29th April 2018] Bollier, D. and Firestone, C.M. (2010)The promise and peril of big data. Washington, DC: Aspen Institute, Communications and Society Program. Chen, H., Chiang, R.H. and Storey, V.C. (2012) Business intelligence and analytics: from big data to big impact.MIS quarterly, pp.1165-1188. Fligler, A., Dayagi, Y., Haleva, A. and Mashal, S., Olista Ltd (2010)Analyzing and detecting anomalies in data records using artificial intelligence. U.S. Patent 7,689,455. Gandomi, A. and Haider, M. (2015) Beyond the hype: Big data concepts, methods, and analytics.International Journal of Information Management,35(2), pp.137-144. George, G., Haas, M.R. and Pentland, A. (2014) Big data and management.Academy of management Journal,57(2), pp.321-326. Krutz, R.L. and Vines, R.D. (2010)Cloud security: A comprehensive guide to secure cloud computing. New Jersey: Wiley Publishing. Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and Muharemagic, E. (2015) Deep learning applications and challenges in big data analytics.Journal of Big Data,2(1), p.1. Nilsson, N.J. (2014) Principles of artificial intelligence. Morgan Kaufmann. Russell, S.J. and Norvig, P. (2016) Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,. Wang, L., Von Laszewski, G., Younge, A., He, X., Kunze, M., Tao, J. and Fu, C. (2010) Cloud computing: a prospective study. New Generation Computing,28(2), pp.137-146.