This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to pollution and the resultant power outages. The primary goal of our study was to increase the precision of regression models for this application area by exploiting additional input attributes extracted from satellite imagery and adjusting the modeling methodology. Given that thousands of different attributes can be extracted from satellite images, of which only a few are likely to contain useful information, we also explored suitable feature selection procedures. We show that a suitable combination of attribute selection methods (relief, FSRF-Test, and forward selection), regression models (random forest models and M5P regression trees), and modeling methodology (estimating field-measured values of target variables rather than their upper bounds) can significantly increase the total modeling accuracy, measured by the correlation between the estimated and the true values of target variables. Specifically, the accuracies of our regression models dramatically rose from 0.12–0.23 to 0.40–0.64, while their relative absolute errors were conversely reduced (e.g., from 1.04 to 0.764 for the best model).

We present a hydro-meteorological scenario and its adaptation to a novel, advanced data integration and data mining platform developed within the project ADMIRE. The scenario uses data mining techniques instead of more common physical models in order to predict phenomena which are not being ordinarily solved in Slovakia - water temperature and discharge wave propagation downstream of a major water reservoir. The scenario is one of a set of use cases, which form the Flood Forecasting Simulation Cascade - a pilot application of ADMIRE. We describe the data integration methodology approach we have devised, and the variables used in data mining training of the scenario.

This article helps establish reliable baselines for document-level sentiment analysis in highly inflected languages like Czech and Slovak. We revisit an earlier study representing the first comprehensive formulation of such baselines in Czech and show that some of its reported results need to be significantly revised. More specifically, we show that its online product review dataset contained more than 18% of non-trivial duplicates, which incorrectly inflated its macro F1-measure results by more than 19 percentage points. We also establish that part-of-speech-related features have no damaging effect on machine learning algorithms (contrary to the claim made in the study) and rehabilitate the Chi-squared metric for feature selection as being on par with the best performing metrics such as Information Gain. We demonstrate that in feature selection experiments with Information Gain and Chi-squared metrics, the top 10% of ranked unigram and bigram features suffice for the best results regarding online product and movie reviews, while the top 5% of ranked unigram and bigram features are optimal for the Facebook dataset. Finally, we reiterate an important but often ignored warning by George Forman and Martin Scholz that different possible ways of averaging the F1-measure in cross-validation studies of highly unbalanced datasets can lead to results differing by more than 10 percentage points. This can invalidate the comparisons of F1-measure results across different studies if incompatible ways of averaging F1 are used.

Public organisations often face knowledge management problems caused by organisational mobility, the continual and pervasive movement of staff between units and departments. This can be a strength, but also introduces problems of loss of experience and reduced efficiency and effectiveness in working. The Pellucid project is developing a knowledge management system to assist in such situations. The project’s three pilot applications are described, and an abstracted view of their knowledge management needs is developed. An outline of key parts of the technical solution is given.

Data Grids are becoming a popular infrastructure for distributed, data intensive scientific application. Data access optimization is one of the key features of such systems, as the transfer and access to the huge amounts of data are often the bottleneck of the data intensive tasks. Concept of replication, creation of multiple copies of a data source across multiple grid nodes, was adopted by grid community to increase data availability. In this paper, we present the ongoing work on the grid replica optimization framework that addresses both short-term and long-term replica optimization problems. We describe high level architecture of the optimization framework and discuss the promising results achieved by prototype implementation of several system components.