Data Intensive Software Technologies and Applications (DISTA)

The research group Data Intensive Software Technologies and Applications studies data-driven approaches, such as machine learning, artificial intelligence, and big data, to automate and improve software development stages. DISTA is a core research area within the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA).

Our research

The research group Data Intensive Software Technologies and Applications (DISTA) studies data-driven approaches such as:

  • Machine learning (ML) and artificial intelligence (AI) use (big) data to automate tasks such as reasoning, planning, deciding, and predicting.
  • Software and information analysis consider 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 automate the implementation of smarter systems or even generate components of these systems.

Machine learning and artificial intelligence

With the concept of decision algebras, DISTA has suggested a unifying theory for classification approaches. With the concept of aggregation that condenses multi-dimensional, partially ordered data to a totally ordered score, DISTA has opened a new branch of unsupervised machine learning.

Our research also focuses on applying technologies such as statistical concepts, supervised and unsupervised learning, classification, regression, clustering, etc., to real-world problems in engineering, science, and society.

Software and information analysis ...

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

DISTA pioneered the application of measurement and testing techniques to assure information quality (IQ). With our industry partners, Sigma Technology and Softwerk, DISTA have applied this quantitative approach to IQ to numerous real-world documentations.

Scalable computing technologies ...

... such as parallel computing, refers 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.

With context-aware composition, a technique for self-optimizing software systems based on profiling data and ML, and with the first ever fully block-free garbage collector, we have significantly contributed to scalable computing.





External doctoral students

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