- 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.
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.
- 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.