Our research covers both experimental and theoretical aspects of information systems. Our research fields may be divided into six main groups of interest. In practice the groups usually overlap and the research is carried out on the intersection of their topics.

Social Network Analysis

Social Network (SN) is a structure of individuals tied by specific types of relationships. New sources of large amount of data for Social Network Analysis (SNA) became available with the dawn of social network services. The main, recent areas of our research in the domain of SN include:

  • Analysis of multi-layered social networks; measures in multi-layered SNs; clustering and prediction of groups, etc.
  • Extraction of multi-layered social networks from data about common activities and mutual communication of users
  • Classification in social networks, collective classification
  • Social networks in evaluation of organizational structures
  • Latent knowledge acquisition based on social relationships
  • Application of social network analysis in marketing

Semantic Information Retrieval

Traditional Information Retrieval (IR) methods are roughly adequate for modern Web search and analysis. We focus on IR methodologies for current (Web 2.0) or even future (Web 3.0) search and analysis engines. The techniques range from link structure analysis to using social network relationship semantics. We use and research paradigms and technologies like:

  • Semantic Web (OWL)
  • Linked Data (RDF. SPARQL)
  • Web ontologies (FOAF)
  • Web data aggregation

Multimedia Information Processing

Since its early days hypertext has been used in association with multimedia (hypermedia), therefore different types of multimedia information are key ingredients of Web-based information systems. Our research covers the following aspects of the information processing:

  • Audio signal processing
  • Image recognition and video clustering
  • Lossy and lossless compression
  • Platform independent playback support

Database and Data Warehouse Systems

Large databases and data warehouses are used mainly for data mining, particularly from Web sites (Web mining). Our research areas include:

  • Negative association rules
  • Indirect association rules
  • Behavior pattern analysis for users of Web sites and Intranets
  • Business applications of data mining methods

System Performance Analysis and Improvement

System performance and responsiveness are usually crucial issues for users, especially in Web environment. Constant system development should always be led in parallel with performance analysis. Our research in the field covers:

  • Content caching techniques
  • Usability testing
  • Content indexing algorithms
  • Web-based optimization techniques and best practices

E-Learning Methodologies

Modern e-learning (2.0) focuses mostly on Computer Supported Collaborative Learning (CSCL). Using moodle (StOPKa3) as a primary tool for teaching is a great incentive for us for exploring new techniques and applications of online collaboration. Research areas in this field include:

  • Applications of online collaboration paradigms, like wiki and data (video) conferencing
  • Learning Management Systems (LMS) and Learning Content Management Systems (LCMS)
  • Digital documentation techniques, like screencast (with a “talking head” window) and annotated (narrated) screenshot slides
  • Examining based on real-time quizzes

The results of our research are published in international journals and other serial forms listed on so called Philadelphia List. For full publication list see Publications page.