A New Discipline Is Born: Processing Super-size Data
Will van der Aalst and Laboratory of Process Aware Information Systems staff
What influence does big data have on today’s world? What is a ‘data scientist’ and what skills does one need? Those were the subjects of a lecture at HSE by HSE honorary professor Will van der Aalst, from Eindhoven Technical University (Netherlands).
Professor Will van der Aalst has a longstanding history of working with HSE, and was involved in creating the Laboratory of Process-Aware Information Systems (PAIS Lab) MA programme, which he supervised until 2014. In addition to heading the IEEE Task Force on Process Mining, which includes over 50 universities, research centers and IT companies, Professor van der Aalst collaborates on integrating research at HSE and global research networks. He is currently involved in joint research projects and preparing findings for publication with colleagues from HSE.
Professor Will van der Aalst is one of the leading specialists in the modeling and analysis of information systems. He is also the creator and leader of a new, in recent years fast-growing, discipline – process mining. His ideas have had a great impact on researchers and programmers.
What is big data? What role does it play in today’s world?
In just a matter of a few years our society has moved from analogue to a fully digital existence. 10-20 years ago computers were still viewed as specialist machines only available to professionals. Now we all use a smartphone or a tablet, pay for purchases by card, order tickets over the internet and so on.
These significant technological breakthroughs had an astonishing impact on science, business, and daily life. All the information systems we have today are constantly producing massive volumes of data: big data. Everything everywhere is recorded all the time. Transport companies, insurance companies, banks and other businesses base their operations on process oriented information systems. Every one of us, when we buy coffee, send an email, have a phone call, or fill our cars, are involved in a massive data collection exercise. The things around us also produce data: just the iPhone alone contains over 14 sensors for receiving and processing information. Every year data gets bigger and bigger, and the volume increases in line with Moore’s law for transistors. Their overall volume on average doubles every two years. Although the technology for data storage and processing has made great strides forward since the 1960s, they cannot keep up with the pace at which data size and volume is growing.
It is becoming clear that a whole new discipline is emerging – processing supersize volumes of data. As in computer sciences, which grew from applied and fundamental mathematics as computers were developed, data science is developing rapidly, and there is a plethora of data to study.
Researchers world over face the task of effectively using the massive volumes of data available. This could, potentially, make commercial and state companies more efficient in their operations and improve daily life for all of us.
A profession that’s guaranteed to be ‘in demand’
If we are to deal with these data analysis issues, then universities must train people in fundamentally new programmes. That is why this composite profession of ‘data researcher’ has emerged globally.
Data scientists must have new skills and knowledge drawn from several different areas: computer sciences and programming, mathematical methods, and also business administration and management. These composite professions will always be in high demand, but also difficult to master. Key data analysis methods include computer-aided learning, data mining, process mining, visual analytics, time series analysis, and others.
Thus, data science is filling the gap between classical mathematics and applied computer sciences. Today, data scientists face numerous open questions. Some have been around for a while, but others have only just recently emerged. They include:
- How can we analyse data in real time without interrupting the process?
- How can we avoid clear and hidden discrimination in data analysis?
- How can we answer questions that haven’t been asked yet?
- How can we solve data analysis issues while keeping data private?
- Who makes the final decisions: what role does the expert play?
- What is the impact of correlation and cause-effect connections?
These questions are still waiting for scientists to start investigating them.
Graduate students and students should think about whether they might enjoy a career in data analysis, which is sure to be in demand for the next 10 – 20 years. Data scientists, in the near information future, will occupy a similar position to other professionals we see today, such as programmers and information systems architects.
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