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.
Connection to Linnaeus University Centre for Data Intensive Sciences and Applications (DISA)
The Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) is one of the university’s profiled research areas. DISA studies open questions in the collection, analysis, and use of large data sets. With its core in computer science, it takes a multidisciplinary approach and collaborates with researchers from e.g. forestry, mechanical engineering, and e-health. The DISTA research group contributes to the DISA core technologies enhancing e.g. machine learning and scalable computing.
We directly contribute to the DISA research areas of
- AdaptWise with context-aware composition,
- e-health with computer vision and deep learning for the analysis of human motions used in eldercare and high-performance sports, and with efficient data selection preprocessing methods for medical researchers,
- Forestry with computer vision and deep learning for the analysis of ancient remains to avoid forestry activities there, and for the identification of log fingerprints, and with deep learning for strength grading of sawn timber,
- Smart industry with data analysis and machine learning for predictive maintenance, and
- Digital humanities with the Nordic Tweet Stream providing high-quality data for research in the languages used in Scandinavia.
As an essential part of this research, we build and maintain the High-Performance Computing Center (HPCC) of DISA.
Connecting with industry and society's research needs, we have established and organized the industry graduate school Data Intensive Applications (DIA).
Connection to Smarter Systems
Smarter Systems is a complete knowledge environment focusing on systems and systems engineering. Engineering modern computing systems is complex and operates in uncertain and continuously changing environments. Both systems and the way we engineer them must become more intelligent. That is, they need to adapt and evolve through a perpetual process that continuously improves their capabilities, to deal with the uncertainties and change they face.
Humans learn from experience – machines from data. Hence, data-intensive technologies are core to make systems more intelligent. In addition to the contributions to these technologies, e.g. in machine learning and scalable computing, DISTA contributes to three challenges of Smarter Systems:
- Software technology processes and tools putting together data-driven technologies for smarter engineering of smarter systems.
- Verified guarantees of applications based on data-driven models regarding the accuracy, performance, response time, safety, etc., persisting over time.
- Understanding data-driven models for mastering data-driven applications and for turning artificial back into human intelligence.
Connection to education
DISTA is responsible for the following courses:
Bachelor courses
- 2DV516, Introduction to Machine Learning, 7,5 credits
- 2DV605, Parallel Computing, 7,5 credits
- 2DV50E, BSc Thesis, 15 credits
Master courses
- 4DV507, Code transformation and interpretation, 5 credits
- 4DV652, Project in Data Intensive Systems, 10 credits
- 4DV657, Parallel Computing, 5 credits
- 4DV660, Statistical and Machine Learning, 5 credits
- 4DV661, Deep Machine Learning, 5 credits
- 4DV50E, MSc Thesis, 15 credits
- 5DV50E, MSc Thesis, 30 credits
Projects
Current projects
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Doctoral project: Advanced identification methods for the forest industry through CV/AI The project intends to create opportunities for continued digitalisation in the forest industry. This concerns…
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Doctoral project: AI in administration of agricultural subsidies We want to design, implement and evaluate systems based on artificial intelligence that supports our customer with the administration…
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Doctoral project: AI-powered patent drafting This project builds generative AI tools to improve the workflow of patent attorneys, driving innovation in patent drafting.
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Doctoral project: Big Data Exploitation for Insight into Electrolux Professional Products Lifecycle Management This doctoral project aims to use Big Data to map the life cycle of professional products…
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Doctoral project: Digital twin developments within Volvo CE This doctoral project relates to develop a so-called digital twin platform. The aim is to understand customers' problems and support them…
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Doctoral project: Document Classification and Entity Extraction Many aspects of accounting present difficulties in achieving full automation due to the abundance of unstructured information, such as…
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Doctoral project: Ecosystems and smart cities Cities face major climate challenges. In my research, I investigate how digital transformation and ecosystems contribute to increased collaboration…
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Doctoral project: Enhancing MLOps architectures for efficient integration, deployment and inference of AI Models in diverse industrial settings This project focuses on advancing MLOps architectures to…
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Doctoral project: Machine Learning in Manufacturing Many manufacturing companies struggle with transitioning into the era of smart technologies, due to the fact that modern lab-grown methods and data…
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Doctoral project: RUL prediction based on historical battery cycle logs This project aims to create a workflow with diverse machine learning algorithms to simulate battery remaining useful life (RUL)…
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Project: HPC for SME The aim of the project is to provide small and medium-sized enterprises (SMEs) in the Linnaeus region with the opportunity to enhance their data-driven capabilities with the…
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Project: In-line visual inspection using unsupervised learning The purpose of this project is to introduce and improve machine learning- assessment of the quality of massproduced industrial (steel)…
Concluded projects
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Doctoral project: Efficient detection of changes in software evolution and their operationalization for software maintenance This project aimed at understanding and using changes detected in software…
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Project: SciChallenge Using digital technologies and social media, the SciChallenge project will create a competition to engage more young people in Europe in the areas of natural science, technology…
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Project: Software Technology for Self-Adaptive Systems The purpose of this project is to increase the engineering efficiency of self-adaptive systems. The development, maintenance and operation of…
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Seed project: Automated Assembly/Disassembly Instructions (ADDITION) The project aims at building a consortium interested in laying the foundation for harnessing the power of AI and digitalization for…
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Seed project: Development of an intelligent wearable – the DIWAH study The overall goal of the research in this seeding project within the Linnaeus University Center for Data Intensive Sciences and…
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Seed project: Digitized ancient remains detection The main objective for this seed project within Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) is to explore if…
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Seed project: Investigate Machine Learning Techniques for Decision-Making Support in K-12 Educational Context The main aim of this seed application is to investigate the use and application of Machine…
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Seed project: Machine learning stabilized steady-state advective-diffusive heat transport This seed project aims to explore and use the strengths of Scientific Machine Learning (SciML) to solve the…
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Seed project: Using Artificial Intelligence to Detect Acanthamoeba Keratitis in the eye - the AIDAK study Applicants The overall objective of the research for this seed project within Linnaeus…
Current
News
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Three industrial doctoral students will present their research at AI conference News
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New revolutionary technology to discover problems in industrial production News
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Increased growth potential for companies in the Linnaeus region through new project using artificial intelligence and high-performance computer programs News
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Strengthen your company’s development journey with an externally-employed doctoral student in AI/Computer/IT News
Publications
Staff
- Anna Wingkvist Research advisor
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- Dag Björnberg
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- Daniel Toll Lecturer
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- Felix Viberg
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- Joel Cramsky
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- Jonas Lundberg Associate professor
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- Jonas Nordqvist Senior lecturer, promoted lecturer
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- Manoranjan Kumar
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- Mathias Hedenborg Senior lecturer
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- Mohamad Homam Mawaldi
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- Morgan Ericsson professor
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- Nemi Pelgrom
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- Niels Gundermann
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- Rakhshanda Jabeen
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- Sebastian Hönel Postdoctoral Fellow
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- Tibo James Liam Bruneel
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- Welf Löwe Professor
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External doctoral students
- Oleg Danylenko (Klarna)
- Erik Österlund (Oracle)