This journal article mostly focuses on the topic of the newest technology of Data Visualization with the utilization of Big Data and the techniques that make the data analytics procedures much more important and effectual. It states that the latest technology of Data Visualization enables the smooth handling of Big Data and the analysis of the data generated at a heap, which is otherwise a very complex process, given the available tools in the market. The journal also states that it would be very effective in analyzing Big Data as the other tools for the same purpose are either complex or there is an absence of sufficient expertise regarding the data analysis with the traditional tools. Thus, it can be concluded from this journal, that the Data Visualization can serve the purpose of simplifying data analysis procedure for different industries generating Big Data as a strategic business process.
This journal article focuses mostly on the advancements of the techniques by which Big Data analysis is developing on the basis of automatic identification of relevant data, capturing of data and data storage technologies. The journal states that the traditional technologies for performing these tasks were falling short of expectations since the amount of data generated in any kind of industry for Big Data analysis is shooting up at an exponential level. Over the recent advancement of technological era, it has been found that the traditional way of analyzing Big Data can slowly be discarded and both the practitioners and users can indulge in the latest tools for analyzing big data. This technology can be applied to almost all the industries like business, education, science engineering and also in commercial or construction industries. As per individual discretions, it this journal serves the purpose of mitigating the problems that are faced nowadays due to the voluminous characteristics of the data generated during the analysis of Big Data.
In this journal article, the author suggests an advanced procedure to eliminate the problems that had occurred during the data tsunami of wireless networks working under the 5G. The journal has pointed out that during this time, the current approaches that were followed, including the acquisition of the new spectrums to the deployment of the base stations or BSs were slowly becoming ineffective in terms of the costs, scalability and flexibility. It was thus suggested that the new approaches of catching the data in Big Data analytics for the 5G networks be utilized to mitigate the problems. Having a user discretion on the journal’s basic solution, it can be said that the plan is feasible, however, it does not denote how the same problem be mitigated for other generations of networking or how the future generations of networking can be solved when the similar problems arise.
Advancement of technology and software generated information are given high priority while implementing the Big Data analytics in the successful deployment of intelligent transport systems. Thus, the journal article regarding this topic enlightens how the big data for traffic information is following a shallow prediction model and have been constantly falling short of the real-world application standards. The journal suggests that the traffic flow prediction management should use a much deep architecture model for the traffic data analysis. The journal also points out that the traffic data is generated at an exponential level causes the failure of the traditional models and this can only be solved with a stacked auto-encoder model. Given the problems that are faced in the recent times due to the faulty traffic analysis, it is a feasible plan to implement deeper analytics method for Big Data analysis of the traffic data.
The journal article in discussion focuses more on the advancements of Big Data in the industry of healthcare biomedical informatics. The latest development of introducing Big Data analytics in the entire industry of healthcare and biomedical informatics can be considered a huge development in the adaptation of emerging technologies in the industry. According to the journal article, it has been found that the flow of information is exponentially increasing in every possible way per day. That is why it is becoming extremely problematic to ensure proper analysis of data. The introduction of the Big Data analytics in healthcare industry has been extremely favorable in terms of the gathering, storing and analysis of huge amount of data coming from different resources and being both structured and unstructured in characteristics.
This journal article under review focuses on the matter of the application of Big Data and analytics in Business Intelligence and Business Analytics. It is a recent development in the field of business intelligence that is gradually proving to be efficient in the analysis of data generated from unusual sources and out of the latest technologies. It has been introduced in the field of business during the year 2011, however; in the field of education also, the Big Data analytics are becoming increasingly popular to sort out the data regarding the growing number of students in the business schools. However, it has been predicted by the authors that the true complexity of Big Data will increase with time due to huge incur of data and for that the employees in any industry would have to be more skilled to handle all the analytical data.
This journal article sheds light on the fourth stage of industrialization, commonly known as the Industry 4.0, which uses the entire smart object implementations and vouches for these utilizations as well. The implementation of Big Data analytics in the world of Industry 4.0, with the use of smart objects like the cloud, supervisory control terminals amongst others is a genuinely effective implementation that helps in the analysis of the huge amount of data generated every day. The implementation of smart objects creates a huge storage space for incoming of data, which requires big data analytics for the analysis of the huge amount of data generated every day. For the cyber-physical data generated, this is a much more feasible technology that should be effectively adapted by every organization looking for a upgrade in business.
This journal article focuses on the humongous amount of data that is generated every other day in the industry of healthcare and the effect of adapting big data for the analysis of these data. The journal articles states an issue of increasing amount of population that has been the major reason for the increased amount of healthcare data. It specifies the introduction of the latest technology about wearable data collecting methods and robotics to make the collection of data much easier. The journal article focuses on the fact that just not the analysis of data is important, but it is equally important to collect the data for the analysis method as well.
This journal article under review sheds light on the fact that in further innovations for the Big Data analysis technology can be introduced in the form of amalgamating Software Defined Networking or SDN with that of the Big Data Analytics. These two areas of expertise are traditionally addressed separately, however, it is suggested that SDN can effectively assist Big Data Analytics. From the perspective of user discretion it can be said that this technology adaption would work as an advantage in the world of engineering and other industries as well. This is because; SDN would facilitate efficient network management and configuration of networks that would improve monitoring of network performance which definitely helps the analysis process of Big data.
This journal article puts forward the introduction of the latest technology of implementing cloud with the Big Data analytics. The journal article states that the latest technology depends on four major areas of data analytics, which are, business models, mode scoring and development, data management, user interaction and data visualization. However, this technology has several gaps mentioned within the implementation of the future technology as it was found in the research conducted by the authors of the journal article. This generally might be the disadvantages that cloud computing has along with its advantages, like the security issues regarding management of data. Nevertheless, it is supported that this technology be used to ensure effective analysis on a huge amount of data at once.
The journal article sheds light on the challenges and opportunities of the implementation of Remote Sensing or RS in the current era of Big Data analytics. It states that the increase of the demand for high resolution of big data analytics has implemented the use of the RS in the zone of Big Data analytics. The journal article also states that the RS data gathered is specifically regarded as the RS Big Data. The journal article thus focuses on the data-intensive problems that big data analysis faces with the analysis of RS big data and the latest implementations of techniques that are being used for RS Big Data.
This journal focuses on the most recent development of data gathering techniques for the ease of Big Data analytics through Body Sensor Networks or BSN. This is an emerging technology that is utilized in the zone of Big Data analytics, which specializes in gathering data with operating sensors residing in close proximity of the human body. This technology is gradually being used in the zone of health informatics to gather real-time data for therapeutic and decision making purposes. There are, however; several challenges noticed in the data gathering technique with the utilization of BSN. Nevertheless, this revolutionary innovation eases the real-time data gathering technique for the analysis of Big Data in Healthcare Informatics Industry.
Sample from the Annotation
This journal article mostly focuses on the topic of the newest technology of Data Visualization with the utilization of Big Data and the techniques that make the data analytics procedures much more important and effectual. It states that the latest technology of Data Visualization enables the smooth handling of Big Data and the analysis of the data generated at a heap, which is otherwise a very complex process, given the available tools in the market. The journal also states that it would be very effective in analyzing Big Data as the other tools for the same purpose are either complex or there is an absence of sufficient expertise regarding the data analysis with the traditional tools. Thus, it can be concluded from this journal, that the Data Visualization can serve the purpose of simplifying data analysis procedure for different industries generating Big Data as a strategic business process.
This journal article focuses mostly on the advancements of the techniques by which Big Data analysis is developing on the basis of automatic identification of relevant data, capturing of data and data storage technologies. The journal states that the traditional technologies for performing these tasks were falling short of expectations since the amount of data generated in any kind of industry for Big Data analysis is shooting up at an exponential level. Over the recent advancement of technological era, it has been found that the traditional way of analyzing Big Data can slowly be discarded and both the practitioners and users can indulge in the latest tools for analyzing big data. This technology can be applied to almost all the industries like business, education, science engineering and also in commercial or construction industries. As per individual discretions, it this journal serves the purpose of mitigating the problems that are faced nowadays due to the voluminous characteristics of the data generated during the analysis of Big Data.
In this journal article, the author suggests an advanced procedure to eliminate the problems that had occurred during the data tsunami of wireless networks working under the 5G. The journal has pointed out that during this time, the current approaches that were followed, including the acquisition of the new spectrums to the deployment of the base stations or BSs were slowly becoming ineffective in terms of the costs, scalability and flexibility. It was thus suggested that the new approaches of catching the data in Big Data analytics for the 5G networks be utilized to mitigate the problems. Having a user discretion on the journal’s basic solution, it can be said that the plan is feasible, however, it does not denote how the same problem be mitigated for other generations of networking or how the future generations of networking can be solved when the similar problems arise.
Advancement of technology and software generated information are given high priority while implementing the Big Data analytics in the successful deployment of intelligent transport systems. Thus, the journal article regarding this topic enlightens how the big data for traffic information is following a shallow prediction model and have been constantly falling short of the real-world application standards. The journal suggests that the traffic flow prediction management should use a much deep architecture model for the traffic data analysis. The journal also points out that the traffic data is generated at an exponential level causes the failure of the traditional models and this can only be solved with a stacked auto-encoder model. Given the problems that are faced in the recent times due to the faulty traffic analysis, it is a feasible plan to implement deeper analytics method for Big Data analysis of the traffic data.
The journal article in discussion focuses more on the advancements of Big Data in the industry of healthcare biomedical informatics. The latest development of introducing Big Data analytics in the entire industry of healthcare and biomedical informatics can be considered a huge development in the adaptation of emerging technologies in the industry. According to the journal article, it has been found that the flow of information is exponentially increasing in every possible way per day. That is why it is becoming extremely problematic to ensure proper analysis of data. The introduction of the Big Data analytics in healthcare industry has been extremely favorable in terms of the gathering, storing and analysis of huge amount of data coming from different resources and being both structured and unstructured in characteristics.
This journal article under review focuses on the matter of the application of Big Data and analytics in Business Intelligence and Business Analytics. It is a recent development in the field of business intelligence that is gradually proving to be efficient in the analysis of data generated from unusual sources and out of the latest technologies. It has been introduced in the field of business during the year 2011, however; in the field of education also, the Big Data analytics are becoming increasingly popular to sort out the data regarding the growing number of students in the business schools. However, it has been predicted by the authors that the true complexity of Big Data will increase with time due to huge incur of data and for that the employees in any industry would have to be more skilled to handle all the analytical data.
Critically evaluating the plagiarism report, it can be said that there is no reason that while reviewing the articles I have plagiarized the sentences that the authors have used in their journal articles. I have taken the essence of the writings they have used as well as the basic idea that they were willing to pass on to the audience. With the review done on every single journal article, I have pointed out their positive as well as the negative points, which forms the basic structure of the annotated bibliography. However, talking of originality, I believe that my reviewing of the papers are justified as original discretion of a reviewer, given the fact that the results have pointed out no plagiarized content in my annotated bibliography.
References
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Hu, L., Qiu, M., Song, J., Hossain, M. S., & Ghoneim, A. (2015). Software defined healthcare networks. IEEE Wireless Communications, 22(6), 67-75.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2015). Traffic flow prediction with big data: A deep learning approach. IEEE Trans. Intelligent Transportation Systems, 16(2), 865-873.
Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A., & Jie, W. (2015). Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems, 51, 47-60.
Poon, C. C., Lo, B. P., Yuce, M. R., Alomainy, A., & Hao, Y. (2015). Body sensor networks: In the era of big data and beyond. IEEE reviews in biomedical engineering, 8, 4-16.
Talia, D. (2013). Clouds for scalable big data analytics. Computer, 46(5), 98-101.
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158-168.
Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … & Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.
Zeydan, E., Bastug, E., Bennis, M., Kader, M. A., Karatepe, I. A., Er, A. S., & Debbah, M. (2016). Big data caching for networking: Moving from cloud to edge. IEEE Communications Magazine, 54(9), 36-42.
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