How to represent uncertainty resulting from big data analytics.
While this week\’s topic (see 2 references below) highlighted the uncertainty of Big Data, the author identified the following as areas for future research. Pick one of the following for your Research paper:
Additional study must be performed on the interactions between each big data characteristic, as they do not exist separately but naturally interact in the real world.
The scalability and efficacy of existing analytics techniques being applied to big data must be empirically examined.
New techniques and algorithms must be developed in ML and NLP to handle the real-time needs for decisions made based on enormous amounts of data.
More work is necessary on how to efficiently model uncertainty in ML and NLP, as well as how to represent uncertainty resulting from big data analytics.
Since the CI algorithms are able to find an approximate solution within a reasonable time, they have been used to tackle ML problems and uncertainty challenges in data analytics and process in recent years.
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 four 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.
—References—-
Marcu, D., & Danubianu, M. (2019). Learning Analytics or Educational Data Mining? This is the Question.BRAIN: Broad Research in Artificial Intelligence & Neuroscience, 10, 1–14.
Hariri, R.H., Fredericks, E.M. & Bowers, K.M. (2019). Uncertainty in big data analytics: survey,opportunities, and challenges. Journal of Big Data, 6: 44. https://doi.org/10.1186/s40537-019-0206-3
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