This article introduces a n-gram-based approach to automatic classification of Web services using a multilayer perceptron-type artificial neural network. Web services contain information that is useful for achieving a classification based on its functionality. The approach relies on word n-grams extracted from the web service description to determine its membership in a category. The experimentation carried out shows promising results, achieving a classification with a measure F=0.995 using unigrams (2-grams) of words (characteristics composed of a lexical unit) and a TF-IDF weight.

Shallow lake wetlands, for which aquatic plants (macrophytes) and water clarity are the key indicators of ecological status, provide valuable services to wildlife and humanity. Conservation of these ecosystems requires development of rapid and large scale monitoring strategies, where remote sensing and Geographic Information Systems (GIS) can be advantageous. In this study, high spatial resolution Quickbird and IKONOS and medium spatial resolution Landsat and Aster images were used for monitoring the aquatic plants and water clarity in Lakes Mogan and Eymir. Classification of emergent plants with high spatial resolution data yielded overall accuracies greater than 90% for both lakes, while overall accuracies obtained from the medium spatial resolution data ranged between 80% and 93% for Lake Mogan and between 70% and 78% for Lake Eymir. It was found that there was 23ha reed bed loss in Lake Mogan between 2002 and 2005 and an additional 14ha was lost between 2005 and 2006. In Lake Eymir, no significant change in reed bed area was detected from high spatial resolution images / however medium spatial resolution images revealed 8ha of change which was attributed to the presence of mixed pixels due to low resolution. The overall accuracies for submerged plant coverage classification from Quickbird images in Lake Mogan were 83% (2005) and 79% (2006) and for classification of submerged plants species were 72% (2005) and 69% (2006). Moreover, it was found that blue band together with the ratio of red band to blue band, were the best predictors of Secchi disc depth.

This doctoral thesis deals with the issue of solving massive tasks in GIS. These tasks process large volumes of geographic data with different formats. The thesis describes a theoretical analysis of the complexity of tasks and the possibilities to optimize sub-processes which lead to an acceptable solution. It considers the possibility of using parallelism in GIS, which leads to an acceleration in the processing of large volumes of geographic data. It also proposes a method for the optimization of processes through an algorithm which determines the number of means necessary for the successful solution of a task at a specified time and assigns processes to these means. Additionally, there is a proposed algorithm for the optimization of the preparation of data for extensive GIS projects. The algorithms have been validated by the results of a research project, the aim of which was to analyse the terrain surface above a gas line in the Czech Republic. The primary method of analysis was the classification of an orthophoto image, which was further refined through filtration using the ZABAGED layers. Therefore, the thesis deals with the possibility of improving the results of image classification using GIS instruments as well as dealing with the determination of the error rate in analysis results. The results of the analysis are now used for the strategic planning of maintenance and the development of gas facilities in the Czech Republic. The results of the work have general importance regarding the performance of other operations of the same class in GIS.

This paper deals with the information retrieval in research portals with the intention of the retrieval in scientific publications. We define concepts related to the information retrieval, classification and knowledge representation. We also present existing search tools used as the initial inspiration for the design of the search intergace. Futhermore we describe the implementation as well as the process of collecting sample data. In the last chapter we discuss usability of the developed web application.