WEEK2-ResearchPaper-Data Science & Big Data Analy

Course: Data Science & Big Data Analy

LATE SUBMISSION WILL NOT BE ACCEPTED BY PROF.

Due Date – 1 day

Discussion Question:  Big Data Analytics  

The  recent advances in information and communication technology (ICT) has  promoted the evolution of conventional computer-aided manufacturing  industry to smart data-driven manufacturing. Data analytics in massive  manufacturing data can extract huge business values while it can also  result in research challenges due to the heterogeneous data types,  enormous volume and real-time velocity of manufacturing data.

For  this assignment, you are required to research the benefits as well as  the challenges associated with Big Data Analytics for Manufacturing  Internet of Things.

Prof. Guidelines 

Your paper should meet these requirements: 

• Be approximately four to six pages in length, not including the required cover page and reference page.


• Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.


• Support your answers with the readings from the course and at  least two scholarly journal articles to support your positions, claims,  and observations, in addition to your textbook. The UC Library is a  great place to find resources.


• Be clearly and well-written, concise, and logical, using excellent  grammar and style techniques. You are being graded in part on the  quality of your writing.

Reading Assignments 

AYANI, S., MOULAEI, K., DARWISH KHANEHSARI, S., JAHANBAKHSH, M.,  & SADEGHI, F. (2019). A Systematic Review of Big Data Potential to  Make Synergies between Sciences for Achieving Sustainable Health:  Challenges and Solutions. Applied Medical Informatics, 41(2), 53–64.  Retrieved from http://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=a9h&AN=138949499&site=eds-live 

Dai, H.-N., Wang, H., Xu, G., Wan, J., & Imran, M. (2019). Big  Data Analytics for Manufacturing Internet of Things: Opportunities,  Challenges and Enabling Technologies. https://doi.org/10.1080/17517575.2019.1633689 

Dash, S., Shakyawar, S.K., Sharma, M., Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data 6, 54. http://doi.org/10.1186/s40537-019-0217-0 

Books and Resources 

Required Text

Eyupoglu, C. (2019). Big Data in Cloud Computing and Internet of Things. 2019   3rd International Symposium on Multidisciplinary Studies and  Innovative  Technologies (ISMSIT), Multidisciplinary Studies and  Innovative  Technologies (ISMSIT), 2019 3rd International Symposium On, 1–5. https://doi.org/10.1109/ISMSIT.2019.8932815

L. Zhao, Y. Huang, Y. Wang and J. Liu, "Analysis on the Demand of Top   Talent Introduction in Big Data and Cloud Computing Field in China   Based on 3-F Method," 2017 Portland International Conference on   Management of Engineering and Technology (PICMET), Portland, OR, 2017,    pp. 1-3. https://doi.org/10.23919/PICMET.2017.8125463

Saiki, S., Fukuyasu, N., Ichikawa, K., Kanda, T., Nakamura, M.,   Matsumoto, S., Yoshida, S., & Kusumoto, S. (2018). A Study of   Practical Education Program on AI, Big Data, and Cloud Computing  through  Development of Automatic Ordering System. 2018 IEEE  International  Conference on Big Data, Cloud Computing, Data Science  & Engineering  (BCD), Big Data, Cloud Computing, Data Science &  Engineering (BCD),  2018 IEEE International Conference on, BCD, 31–36. https://doi.org/10.1109/BCD2018.2018.00013

Psomakelis, E., Aisopos, F., Litke, A., Tserpes, K., Kardara, M., & Campo, P. M. (2016). Big   IoT and social networking data for smart cities: Algorithmic   improvements on Big Data Analysis in the context of RADICAL city   applications.

Liao, C.-H., & Chen, M.-Y. (2019). Building social computing   system in big data: From the perspective of social network analysis. Computers in Human Behavior, 101, 457–465. https://doi.org/10.1016/j.chb.2018.09.040

"APA Format" 

https://academicwriter.apa.org/6/ 

"NO PLAGIARISM" 

Plagiarism includes copying and pasting material   from the internet into assignments without properly citing the source   of the material.