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Machine Learning (ML) has used for various innovation and development of new technologies. It is the best way to use ML for the development of new things. In addition, ML is the base of most of the research and development. Besides, Big Data (BD) has used for the management of large volumes of data of various agencies, governments, organizations, and many others. This report will be discussed about the impact of machine learning on BD. In this report, I have used article, which provide role of ML and it will explain using various examples and evidence. In addition, there are some basic concepts behind development of BD and its services.
This report has created based on the article ‘Efficient machine learning for big data: A review’, which was published in Big Data Research journal.
Emerging technologies have used for the management of various things in a proper manner. ML is highly used emerging technology for the development of various innovations as well as manages all the things in a better way. According to Al-Jarrah, et al., (2015), machine learning has used in big data for managing various things. Most of the organization has created their data and information in their information systems and data centers, which the big data and machines learning in the last two decades created. ML has used for accuracy and better performance (White, et al., 2017).
Many things have based on the datasets of the particular department. This article has reviewed two things, which are experimental and theoretical data-modeling literature. The authors have discussed the model efficiency including computational requirements in the learning process, which uses for design. There are many changes in the field of technologies based on new algorithms. The ML has useful for minimizing computational cost of different processes, which has improved its accuracy and stability. In addition, BD involves many things to improve the basic functioning of the system (Packethub, 2018).
The authors have focused on the four things in this article, which are BD, Green computing, Computational modeling, efficient machine learning. According to international surveys, the Information and Communications Technology (ICT) industry produces 2% of total carbon dioxide in the overall world. In addition, energy utilization is necessary for data centers and server computers. It is necessary to reduce emission of carbon dioxide in the environment (Marr, 2015).
The authors have chosen the most popular thing in the world, which has used for the management of other things in a better manner. There are various challenges to overcome emery consumption in the data centers and server computers. Thus, BD has used as a service in a large-scale organization (Lebeaux, 2013). This article has described the big data challenges, which are necessary for the development of various things to overcome the pollution level. In the present time, green computing has used for computational work, which is good for the environment as well as other people. Moreover, BD has included various things for data analysis (Gupta, et al., 2018). In the last two decades, thousands of sensors have installed in the system, which has generated large amounts of data in gigabytes. All the devices have connected with a radio network and they used low power for gathering information on the environment. However, BD is an important part of the business in the present era (Datafloq, 2019).
According to research and survey, those devices have generated data in petabytes. Moreover, the data is necessary for various analyses and decision-making. Therefore, it is necessary to improve basic things. In addition, every year a large amount of data has generated from the various devices, which has used in various operations. It is necessary for the environmental conditions as well as other things. Biological data have used in medical science, which is helpful in the management of various things in a pepper manner. Besides, BD has used for data warehouses management (Chowdhury, 2014).
Besides, Dark Energy Survey (DES) has used a large amount of storage for the digital data volume from the universe, galaxies, and stars. The survey has generated 1.4 terabytes data per night. Thus, it is necessary to use sustainable data modeling and efficient learning. The main objective of research is maximum learning accuracy with minimum computational cost. In addition, there are some basic important things to improve data management and other things. In addition, BD has used for computational data management (Bui, 2018).
The authors have provided details about the ensemble models, which is beneficial for the dataset and data management. It has included Bayes classifiers. Moreover, the Model complexity problem has discussed in detail, which can be resolved by the various approaches. The authors have used a semi-parametric approximation to optimize the various challenges of big data. There are many features of BD, which makes it better for the optimization (Bou-Harb, et al., 2016).
In addition, deep learning is beneficial for optimization. In this article, Shallow learning models have been used to solve well-constrained problems as well as simple problems. In addition, there are many technical concepts has used to provide good things. The authors have explained hierarchical architecture, which has used for pattern classification and representation learning. Deep neural networks (DNNs) have used to manage and utilize a large amount of training data. It has shown great performance in recognition. Big data has used for managing large amounts of data of various devices and sensors (Batty, 2013). The authors have provided some feasible solutions to the energy and data optimization problem using machine learning and BD, which is the best solution to all the challenges (Al-Jarrah, et al., 2015).
Furthermore, BD has provided the solution of data integration, transformation, and extraction, which is a huge task for computer systems (VANTAZ, 2014). Moreover, the data collection and management are the toughest work in the field of computer society, as most of the industries have their personal and official data, which has required a large amount of storage (Prokopp, 2014). Thus, it is necessary to involve internal concepts of neural networks to optimize data and reduces energy consumption in data storage and transformation. Moreover, there are many benefits of data collection but sometimes-large amount of unnecessary data has created issues in front of data management firms.
Moreover, data can be more optimize using ML and other approaches, which are helpful for the other works as well. DNN is the best way to improve internal data and information management. Besides, most of the large-scale organization has used sustainable energy sources and renewable energy sources for their data sources and data warehouses. In this case, energy can be saved for other works. Thus, a system can be used to optimize older data centers using renewable energy sources and other techniques.
Furthermore, there are many benefits of big data in terms of business and operational management. It can provide better outcomes for all the data management processes. It will improve the overall results of most of the processes. In addition, there are some important things, which can be removed using new technologies and systems in an appropriate way. ML has developed new techniques to improve the basic services, which are most powerful in the basic processes. It can be empowered all the technologies using advanced algorithms.
In conclusion, it has concluded based on the various parts of the selected article that big data has used for data collection and management. In addition, machine learning and deep learning has used for optimizing the data gathering and management process through BD. ML is helpful for the development of basic services of big data and it can be better with advance technologies. It is beneficial for business as well.
This article review has a critical analyzed role of machine learning on big data. MLBD is a huge concept, which has merged various things to improve data management and it can be beneficial for the various solution of challenges and issues in energy consumption using data centers and server computers. Finally, MLBD is a huge factor, which can improve the climate changes and other issues. Sustainable modeling and e-science can be beneficial for resolving the issue of a large amount of data management
Al-Jarrah, O. Y. et al., 2015. Efficient machine learning for big data: A review. Big Data Research, 2(3), pp. 87-93.
Batty, M., 2013. Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), pp. 274-279.
Bou-Harb, E., Debbabi, M. & Assi, C., 2016. Big data behavioral analytics meet graph theory: on effective botnet takedowns. IEEE Network, 31(1), pp. 18-26.
Bui, n., 2018. Big Data As A Service: IaaS, PaaS And SaaS. [Online] Available at: https://blog.panoply.io/big-data-as-a-service-iaas-paas-and-saas[Accessed 25 October 2019].
Chowdhury, S., 2014. Big data and data warehouse augmentation. [Online] Available at: https://www.ibm.com/developerworks/library/ba-augment-data-warehouse1/index.html
Datafloq, 2019. Big Data Analytics Paving The Path For Businesses With More Informed Decisions. [Online] Available at: https://datafloq.com/read/big-data-analytics-paving-path-businesses-decision/6110
Gupta, . A. et al., 2018. Big data & analytics for societal impact: Recent research and trends. 20(2), 185-194.. Information Systems Frontiers, 20(2), pp. 185-194.
Lebeaux, R., 2013. big data as a service (BDaaS). [Online] Available at: https://searchcio.techtarget.com/definition/big-data-as-a-service-bdaas[Accessed 25 October 2019].
Marr, B., 2015. Big Data-As-A-Service Is Next Big Thing. [Online] Available at: https://www.forbes.com/sites/bernardmarr/2015/04/27/big-data-as-a-service-is-next-big-thing/#21cd88c233d5[Accessed 25 October 2019].
Packethub, 2018. Big data as a service (BDaaS) solutions: comparing IaaS, PaaS and SaaS. [Online] Available at: https://hub.packtpub.com/big-data-as-a-service-bdaas-solutions-comparing-iaas-paas-and-saas/[Accessed 25 October 2019].
Prokopp, C., 2014. Big Data Science and Cloud Computing. [Online] Available at: https://www.semantikoz.com/blog/big-data-as-a-service-definition-classification/[Accessed 25 October 2019].
VANTAZ, 2014. Big Data Analytics : the Hottest Disruptive Technology in Mining Right Now?. [Online] Available at: https://vantaz.com/big-data-analytics-hottest-disruptive-technology-mining-right-now/
Verma, A., 2018. The Relationship between IoT, Big Data, and Cloud Computing. [Online] Available at: https://www.whizlabs.com/blog/relationship-between-iot-big-data-cloud-computing/
White, G., Ariyachandra, T. & White, D., 2017. Big Data, Ethics, and Social Impact Theory-A Conceptual Framework. Journal of Management & Engineering Integration, 10(1), pp. 22-28.
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