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 the following requirements:
• Be approximately 3-5 pages in length, not including the required cover page and reference page.
• Follow APA guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
• Support your response with the readings from the course and at least five peer-reviewed articles or scholarly journals to support your positions, claims, and observations. The UC Library is a great place to find resources.
• Be clear with well-written, concise, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.
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