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We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

Innovation and R&D are becoming a prominent part of policies of countries and transnational unions such as the European Union. This is shown in strategy "Europe 2020" established by EU which prompts member states to invest 3 % of their GDP in R&D. R&D expenditure is an important indicator of innovation performance of a country. However, it is not only important to look at R&D expenditure as one aggregate indicator, but to also consider the contributions of various innovation actors to R&D funding. Since fi rms are known to be the main innovation actor that creates the biggest amount of innovation in national innovation system, the paper is focused on fi nancing of business R&D. The aim of the paper is to examine business R&D funding from resources of main innovation actors and to analyze the impact of public support of R&D on private R&D investment in EU member states. The research is based on descriptive statistics as well as panel regression and correlation analysis and cluster analysis of 28 EU member states. Our results suggest that the main source used to fund business R&D comes from business sector, followed by public support and resources from abroad. The cluster analysis resulted in four clusters based on the structure of business R&D fi nancing in the EU countries. The analysis of substitution effect of public support of R&D suggests that public support has a positive effect on private investment in business R&D, with the raise of public support for business R&D of 0.1011 % GDP resulting in 1 % increase in business funded R&D expenditure.

Data quality can be seen as a very important factor for the validity of information extracted from data sets using statistical or data mining procedures. In the paper we propose a description of data quality allowing us to characterize data quality of the whole data set, as well as data quality of particular variables and individual cases. On the basis of the proposed description, we define a distance based measure of data quality for individual cases as a distance of the cases from the ideal one. Such a measure can be used as additional information for preparation of a training data set, fitting models, decision making based on results of analyses etc. It can be utilized in different ways ranging from a simple weighting function to belief functions.

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