Advanced and Efficient Big Data Management and Analysis

The ongoing, sweeping digitalisation of societal and industrial processes represents a development that holds substantial potential for transforming society. Big data is a consequence of this development. Indeed, data has been termed the new oil, and data will play an increasingly important role in society and industry. As a result, it becomes increasingly important to be able to create value from data. This in turn calls for research that addresses challenges caused by big data.

The objectives for this workstream are:

to invent new efficient and effective means of enabling value creation from big data and
to develop software prototypes as a foundation for empirical studies and demonstrations with real-world data in collaboration with users.

The thematic area of Big Data involves the invention, integration, and empirical study of algorithmic techniques, and it involves analytics including machine learning and data mining. As such, Big Data has very substantial interfaces to the thematic areas Artificial Intelligence (in relation to machine learning) and Algorithms and Data Structures. There are also links to the thematic area Cyber Physical Systems and Internet of Things, e.g. in relation to distributed data platforms.

Further, there are links to the thematic area Human Computer Interaction, CSCW and InfoVis, e.g. in relation to the efficient visualisation of massive data. The area Business Innovation, Processes and Model is concerned in part with aspects related to introducing Big Data technologies into organisations. Finally, there are relations to the thematic area Ethics, e.g., in the areas of transparency and privacy.

Projects

Bridge project

Multimodal Data Processing of Earth Observation Data

Based on observations of the Earth, a range of Danish public organizations build and maintain important data foundations that are used for decision-making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark. This project aims to support the digital acceleration of the green transition by strengthening the data foundation for environmental data.

Bridge project

Mobility Analytics using Sparse Mobility Data and Open Spatial Data

The amount of mobility-related data has increased massively which enables an increasingly wide range of analyses. When combined with digital representations of road networks and building interiors, this data holds the potential for enabling a more fine-grained understanding of mobility and for enabling more efficient, predictable, and environmentally friendly mobility.