Data is found everywhere today in our society. This applies to everything from our smartphones and GPS navigation in cars to the sensors mounted on wind turbines. And by analyzing these huge amounts of data you can detect anomalies, which can contribute to improve our state of health and optimize companies’ production.
The concept is called anomaly detection and the 31-year-old Tung Kieu has plunged into this topic.
Can you tell us a bit about your background and how you ended up working with big data and anomaly detection?
I have a Master’s degree in computer science from Vietnam National University, and I have been in Denmark for about five years. I came to Denmark because I received a PhD scholarship in the research group Daisy – Center for Data-Intensive Systems at Aalborg University, which is led by Professor Christian S. Jensen. When I finished my PhD, I became a research assistant, and after a few months, I got a position as Assistant Professor.
Aalborg has a great reputation in computer science and engineering and Christian S. Jensen is furthermore recognized for his outstanding research in databases and data mining. In Vietnam, my supervisor was affiliated with Christian S. Jensen and, in this way, I got in touch with him and received the scholarship.
In what way is research in Denmark different to research where you come from?
– The environment in Denmark is very good and, furthermore, Aalborg is the happiest city in EU, according to a study from EU-Commission. We have a very good work-life balance, where we focus more on the efficiency than the working time. Aalborg University is a young yet very active university. The university ranks very high compared to other universities, and our lab – Center for Data-Intensive Systems (DAISY) ranks 2nd best among all research groups in Europe. It’s great to be part of that.
Can you tell us about your research?
I work with databases and data mining and, more specifically, the area called anomaly detection in data. Due to the extensive digitization of processes, new data are constantly created, and by being able to analyze and utilize data, we can optimize our everyday lives.
However, there are a number of challenges. We produce such large amounts of data all the time that very efficient algorithms are required to analyze for anomalies. In addition, data quality is a challenge because much sensor data is subject to noise and potentially contains incorrect values. This means that you have to clean data to achieve the required quality. But this is also what makes the area interesting.
What do you expect to get out of your research and how can your research make a difference for companies and communities?
It may be easier to understand if I give a few examples. Anomaly detection can be used in many different places. For example, supermarkets collect data about their customers, and we can analyze these data and get an overview of people’s shopping patterns. The supermarkets can use this to customize their purchases so that they do not end up with a lot of products that they cannot sell.
Another example are data collected from sensors installed on wind turbines. Here we can use the algorithms to detect anomalies and thus predict if components in a wind turbine are about to collapse, which is of great benefit to the wind turbine manufacturers.
Today, smartphones are very common and people use them to measure their health and how much exercise they get. We can use these data to analyze people’s health state. When smartphone users record data about their heart rate, we can actually analyze when people will potentially get a heart attack. The possibilities are endless, which makes the research area interesting.
