Data Intensive Software Technologies and Applications (DISTA)

The Data Intensive Software Technologies and Applications research group studies data-intensive technologies such as machine learning and artificial intelligence, software and information analysis, and scalable computing technologies.

Our research

The Data Intensive Software Technologies and Applications (DISTA) labs study data-intensive technologies such as:

  • Machine learning and artificial intelligence use (big) data to automate tasks such as reasoning, planning, deciding, and predicting.
  • Software and information analysis considers the IT systems as data and reflects on their quality.
  • Scalable computing technologies allow coping with the volume of big data sets and the velocity of big data streams.

Altogether, data-intensive technologies are enablers for turning data into information and actionable knowledge that automates the implementation of smarter systems or even generates components of these systems.

Machine learning and artificial intelligence

With the concept of Decision Algebras, the DISTA labs have suggested a unifying theory for all classification approaches. Otherwise, our research focuses more on the application of technologies such as statistical concepts, supervised and unsupervised learning, classification, regression, clustering, etc. to real-world problems in engineering, science and society. An example is the analysis of human motions used in eldercare and high-performance sports. With this research, we also contribute to the research area eHealth of the Linnaeus University Centre for Data Intensive Sciences and Applications.

Software and information analysis ...

... assesses software engineering artefacts like processes, specifications and code. This may be estimating the effort of developing and maintaining software, getting new users to understand structure and behavior of software, finding dependencies and changing impact of sub-systems, assessing the software quality, finding the relevant components for re-engineering, etc.

The application of analysis technologies to software and information quality takes the user viewpoint. Quality can be considered as a measure of the value that software or information provides for the user. Quality is often perceived as subjective, and the quality of software or information can then vary, among users and among uses of the information. Only recently it has been suggested to apply measurement and testing techniques to assure information quality. Together with one of our industry partners, Sigma Technology, the DISTA Labs have pioneered this development. This research area on Data-driven Software and Information Quality is part of the Linnaeus University Centre for Data Intensive Sciences and Applications.

Scalable computing technologies ...

... such as parallel computing refer to technologies enabling scalability of systems to large problems or data sets, e.g., by executing a program on more than one processor or core. We are interested in high-performance computing, distributed computing and stream processing, as used in e.g., scientific and technical data mining.

As an essential part of this research, we build and maintain the High-Performance Computing Center of the Linnaeus University Centre for Data Intensive Sciences and Applications.

Projects

News

Staff

External doctoral students

  • Oleg Danylenko (Klarna)
  • Erik Österlund (Oracle)