Head of Doctoral Programme: Associate Professor Kristian G Olesen
The programme mainly serves the Department of Computer Science (CS). A few PhD students with computer science related topics from the Department of Electronic Systems, Esbjerg branch, and theDepartment of Architecture, Design and Media Technology are also enrolled.
The CS department's research has software, use and performance of software, as well as information and data as its subject. In particular there is research in use of software in organisations, software engineering, management of software engineering, human-computer interaction, programming and languages, data management, data analysis and data mining, techniques for decision support, machine learning, autonomous agents, networks and protocols, techniques and models for distributed and parallel software, and tests.
The research approach is fundamentally constructive and embraces analytical mathematical research, experimental research with algorithms, systems, techniques and methodologies, as well as analytical empirical research.
The department's research is conducted within four research groups, as follows:
Database and Programming Technologies:
The group's research concerns general-purpose programming languages as well as special-purpose languages, e.g., for the management of different types of data, and it covers also languages that aim to integrate programme logic and data management. Also studied are environments that offer integrated tool support for application and programme development. In the area of databases, the research relates to data warehousing and business intelligence, and to temporal, spatial, and spatio-temporal databases, including conceptual modelling and database design, data models, query processing, indexing, and applications. Research related to mobile services and the world-wide-web covers semi-structured data management, location-based and context-dependent mobile services, application development, and XML-related programming. The group's research approach is primarily constructive, integrating experimental and analytical elements. Constructive activities include the design and implementation of concepts, frameworks, data structures, languages, tools, and systems. Experimental activities cover the testing of constructed artefacts, including prototype-based studies and simulation-based performance studies. Analytical activities include complexity analysis and language evaluation. Emphasis is on developing theoretically sound results solving real-world problems.
Distributed and Embedded Systems:
The research of the group concerns modelling, analysis and realization of computer programmes, with an emphasis on distributed and embedded systems. This includes the following areas: 1) Semantic theories for modelling the behaviour of computer programmes and systems; 2) Design, implementation and models for analysis of distributed systems and networks; 3) Algorithms, methods and tools for verification and validation of programmes and systems. Each of these three research areas are subjects in their own right, but are also interrelated in a number of ways: semantic models offer important guidelines for development of languages and paradigms for distributed systems; semantic models are necessary prerequisites for development of verification algorithms and tools; the development of validation tools provides new insight into the underlying semantic models on one hand, and are applied in environments for the construction and analysis of distributed systems; the evolving nature of distributed systems provide insight to the strengths and weakness of existing semantic models, and serve as inspiration for development of new ones; finally, distributed systems truly expose the limits of given verification algorithms.
The Information System Group conducts research on the development and use of information technology. The research field is development and use of computerised systems at two levels: humans and organisations. This is reflected in the two research groups: 1) Human-computer interaction (HCI): design and evaluation of the interaction between a user and a computerised system; 2) Systems development (SD): development and use of computerised systems in organisations. The HCI research deals with design and evaluation of the specific interaction between a user and an interactive computerised system. The goal is to improve design and usability evaluation. This involves design of user interfaces of interactive systems for specific applications. It also involves usability evaluations of specific interactive systems in order to provide a basis for improving the design of these systems. The SD research deals with systems development, the organisational and social aspects of systems use, IT management, and use of IT in innovation and change. The target of the research is the professional practitioner engaged in the development and use of software and information systems in the broadest sense, e.g., developers, project managers, and IT managers. The research seeks to improve professional practitioners' ability to engineer systems as well as their ability to plan and manage effective social and organisational interventions.
The Machine Intelligence group conducts research on intelligent reasoning and decision making under uncertainty, as well as statistical methods for machine learning and data mining. A common basis for many of the research activities in the group are graphical probabilistic models, especially Bayesian networks and influence diagrams, which allow compact representations and efficient inference algorithms for probabilistic and decision theoretic models. Bayesian net-works find application in several areas. They are used, for example, as diagnostic models in tech-nical and medical domains, for modelling genetic relationships in bioinformatics, and for modelling unknown environments in robot navigation. The Machine Intelligence group pursues research acti-vities in three main areas: 1) Probabilistic Graphical Models: developing efficient design and inference methods for graphical models; 2) Machine Learning and Data Mining: statistical methods for learning, especially learning of graphical models, and methods for solving data mining problems like clustering and classification; 3) Autonomous Agents: using graphical models for programming intelligent behaviour in autonomous agents, e.g., autonomous agents in computer games.