Research directions

1. EDA - Exploratory Data Analysis

   Exploratory data analysis means open-minded looking at data with the aim to detect and describe patterns, trends, and relationships and to generate hypotheses, which can then be tested using mathematical methods. The very nature of EDA implies that data visualisation plays a crucial role. As is defined in a dictionary, "visualise" denotes "to make perceptible to the mind or imagination". In EDA, it is the mind of a human explorer that is the primary tool of analysis. It is the task of the mind to detect and describe patterns and to generate hypotheses. However, the mind can fulfil its mission only if the data to be explored are made perceptible to it. No thinking is possible without prior perception. Hence, data visualisation is the most important supporting tool (i.e. supplementary to the human mind) in EDA.

   Currently we focus on the following aspects of exploratory visualisation:

   • Systematic design of novel visualisation methods based on comprehensive analysis of properties of data and relevant data analysis tasks;
   • Scalable visualisation: design of visualisation methods and tools that can be applied to large and multidimensional datasets;
   • Knowledge-based approaches to data analysis for helping users to work with powerful but, for this reason, indispensably complex visualisation systems;
   • Knowledge construction: approaches to support users in registering the observations they make in the course of data analysis, managing and structuring such observations, checking hypothesis, and reporting findings to others.

2. DCV - Decision-Centred Visualisation

   Decision-centred visualisation (DCV) means the usage of problem-oriented domain knowledge for intelligent data search, processing, analysis, and visualisation in time-critical applications. An ultimate goal of DCV is to optimize the overall decision-making process and improve the quality of decisions on the basis of intelligent visualisation and related scientific disciplines and technologies. DCV is usually applied in conflict domains such as military mission planning, counter-terror intelligence etc. DCV is used in these areas as a core component of modern control rooms.

   We do not restrict our research to only these domains but consider decision-centred visualisation more generally as "give everybody the right information at the right time and in the right way". Respectively, we focus on two topics:

   • Knowledge-based visualisation
   • Informed decision making on the basis of comprehensive data analysis enhanced by computational methods such as data mining, statistics, and multi-criteria decision analysis

3. VIC - VIsual Comminication

   One of problems that prohibits a wide use of visualisations is the problem of reporting. Interactive visualisation is nice to play with, but it is difficult to collect and report the results of data analysis. Therefore, it is necessary to design and develop principles and methods of computer-guided data analysis and decision making followed by efficient presentation of discoveries and decisions.

   We focus on the following aspects:

   • Selection of relevant information according to the purposes of communication and the role and interests of the intended recipient
   • Presentation of the selected information on an optimal level of detail and in an appropriate visual form, which depends on the character and properties of the information, the goal of communication, and the characteristics of the intended presentation medium.

Visual analytics is a multidisciplinary field that includes the following focus areas:

• Analytical reasoning techniques;
• Visual representation and interaction techniques;
• Data representations and transformations;
• Techniques to suport production, presentation, and dissemination of the results of analysis.