DESS - Data Engineering, Science and Systems

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, including spatio-temporal, multidimensional, time series, sensor, and graph data, with applications in a broad range of areas, including intelligent mobility, digital and renewable energy, smart buildings, and greenhouse gas emissions reduction.

About DESS

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.

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 studies of the properties of the artifacts.

Scope

Data

Topics

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.

Applications

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 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

Research approach

The research approach is primarily constructive: theoretically well-founded, purposeful artefacts such as frameworks, data structures, indexes, algorithms, languages, tools, and systems are designed, and proof-of-concept prototypes are constructed in order to carry out empirical studies of the properties of the underlying artifacts.

Further, the research is often driven by novel and challenging real-world applications, with primary applications in intelligent transportation and digital energy.

The research has impact on (at least) SDGs 3, 4, 6, 7, 11, 12, and 13.