Executive Summary

Recent technology allows for the collection of massive volumes of data. The challenge of transforming these data into valuable and actionable information is an activity known as big data (or data science). These datasets are not only huge, but complex, including unstructured, heterogeneous data, image, video, human language, and hence entirely innovative approaches are needed to handle them. Big data analytics is an enormous worldwide research effort aimed at taking the guesswork out of decision making in society.

Data Science is an emerging discipline that combines data management, machine learning, data analysis, artificial intelligence, data visualization and user experience design to big data.

At the Big Data @ Vestlandsforsking, we take this deluge of data and make sense of it. We explore ways about how best to use it for the benefit of society. We process and use information to empower better decision making for citizen, society and business. Moreover, we concerned with the study of different aspects of data science – seen as a crucial  scientific discipline – and develop methods, tools and applications for its effective deployment in real world problems. From an application point of view, we are concerned with a variety of problems, ranging from smart cities, to emergency management, to social content monitoring, to social engagement.

In parallel, our research also addresses prominent issues around privacy, data protection and ethics, in order to apply big data to solve challenging problems for society. Big Data @ Vestlandsforsking supports international, national and regional collaboration and knowledge transfer in this field.

On-going Projects

Leveraging Big Data for Managing Transport Operations 

The project received funding from Horizon 2020.

Big Data and Emergency Management

The project received funding from the Research Council of Norway (RCN) and the Norwegian Centre for International Cooperation in Education (SiU) through INTPART programme.

Ubiquitous Data-Driven Urban Mobility

The project received funding from the Research Council of Norway (RCN) through IKTPLUSS programme.