AIM AND SCOPE
Big Data science has the ability to identify unexpected relations in results, because the mechanisms and phenomena involved, as well as their combined workings, may be different than foreseen by experts. Consequently, two extreme approaches utilising machine learning emerge: data-driven discovery and data-driven modelling. The first one completely avoids models based on previously established insights into the systems at hand, and instead describes the processes entirely based on acquired data. The second approach employs machine learning algorithms in order to identify new relations in data, and only use these new relations to re-assess, reformulate and enhance previously established understanding. Since the second approach has the potential to give a better understanding of reality, and is based on the understanding already present in a research domain, DRIVEN employs this second approach to
- harvest new insights in specific research fields, and
- equip the domain scientists with a solid data science background,
enabling them to generate unprecedented insights in their areas of interest.