Behind the data
In the last decade, social networking has gained huge momentum. People have have begun relying on it for gathering information, keeping informed with recent news, and hearing other users’ opinions on diverse topics. Such reliance generates massive data – so big, in fact, that it’s difficult to analyze using traditional data analysis methods.
That’s where Assistant Professor Rasha Kashef and her research in machine learning come in.
Machine-learning techniques have the capacity to analyze big data and can be used to detect useful knowledge like trends, patterns, and rules.
“Let’s say you have 1,000 Facebook friends and you’re seeking expert advice on an outfit,” Kashef explains. “You want to know which of your friends works in fashion without having to look through all the web documents – in this case, Facebook profiles. You just want to ask that small, specific group. We provide the tool to do this.”
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