Data Engineering, Science and Systems



Data Engineering, Science and Systems (DESS) is one of four research groups in Department of Computer Science, Aalborg University.

DESS encompasses up-and-coming scientists as well as experienced scientists, all with strong records of excellence and contributions in data engineering, data science, and data systems that span up to 30 years. Embracing the opportunities enabled by the ongoing, sweeping digitalization of societal, industrial, and scientific processes, we collaborate with our partners to conduct research that aims to advance value creation from data.


Targeting value creation from data, the research is generally constructive in nature, meaning that the research concerns:

  • The invention of purposeful artifacts, such as frameworks, algorithms, data structures, indexes, languages, and techniques, as well as tools, systems, and solutions based on such components.
  • The construction of prototype software, typically either for proof-of-concept or for the purpose of conducting empirical studies.


The research spans foundational topics as well as advanced applications and aims to take advantage of opportunities for cross-fertilization between foundational and applied research activities. The research also embraces a data science approach, where solutions to specific domain challenges are invented.


The research focuses on spatio-temporal, multidimensional, timeseries, sensor and metric data, but also contributes in relation to graph, text, and IoT, and electroencephalogram (EEG) data.


The research concerns data management and analytics, including query processing, data mining, and machine learning. Examples include:

  • Data management: data integration and data lakes, data warehousing, and indexing
  • Analytics: prediction and forecasting, pattern and outlier detection, similarity search, advanced routing, transfer learning, greenhouse gas emissions estimation, spatial keyword querying, clustering, why-not querying


While key applications are in the general areas of intelligent transportation and digital energy, the group covers a wider range of data-intensive applications. Example applications include:

  • Flexible energy grids that enable the ongoing transformation of the electricity grid and society-wide electrification.
  • Smart services for energy efficient buildings
  • IoT data-based diagnostics and prediction in the renewable energy sector
  • Adaptability and failure prediction in power electronics
  • Learning of travel times and travel-time based routing in dynamic and uncertain road networks
  • Advanced spatial crowdsourcing in transportation and beyond
  • Maritime analytics, including energy efficient routing and speed recommendation
  • Personal data retention and GDPR compliance
  • EGG based data analysis pipelines for neurorehabilitation, motion intent detection, and emotion and activity recognition
  • Analysis of complex microbial community interactions

For more information see

DESS webpage


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