Research group
Data engineering, science og systems (DESS)
Research group
Data engineering, science og systems (DESS)
Research
Key research areas
The DESS research group targets the following overall challenges:
- Efficient and effective analytics, including query processing, data mining, and machine learning for temporal, spatio-temporal, and time-series data, enabling value creation in a broad range of domains, including transportation, Internet of Things (IoT), and digital energy
- Big data analytics, including data warehousing, OLAP, and data mining for multidimensional and time series data, enabling value creation in, e.g., intelligent energy applications.
Education
Study related activities
The DESS staff teaches 15-20courses, including data management, and third semester M.Sc. specialization courses.
Furthermore,we are involved in the Bachelor’s and Master’s programmes in data science and organize PhD courses in data management and machine learning.
Collaboration
Who benefits from the research
Our research benefits most directly companies and organizations that manage temporal, spatio-temporal, and time-series data, but also graph, textual, heterogenous, and energy data. This includes companies and organizations - often within transportation, IoT, and digital energy - that work with location-based services, travel-time information, green accounting, and control of energy usage.
External partners
Bring Logistik AB, EWII, FlexDanmark, IBM Research Ireland, IT Universitetet, Nanyang Technological University, TU Dresden, University of Zurich and Zhejiang University.
Key Projects
DIREC
National Centre for Research in Digital Technologies aims to expand the capacity within research and education in Denmark.
OPTITRUCK
The project focuses on fuel savings in +40-ton trucks.
DEDS - DATA ENGINEERING FOR DATA SCIENCE
Joint PhD education focusing on research in methods and tools for big data analytics
FED - flexible energy denmark
The project is targeting a more costeffective green transition through intensive use of data.
A data-intensive paradigm for dynamic, uncertain networks
Data analytics and machine learning on massive trajectory data for greener and more efficient transportation.
Contact
Read about more research groups
At the faculty, we have more than 30 research groups and sections with internationally recognized researchers who work in the areas of: planning, digitization, autonomous systems, communication and human touch.