Data Mining for Browsing Patterns in Weblog Data by Art Neural Networks

Categorising visitors based on their interaction with a website is a key problem in Web content usage. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customised content. This paper proposes an approach to clustering weblog data, based on ART2 neural networks. Due to the characteristics of the ART2 neural network model, the proposed approach can be used for unsupervised and self-learning data mining, which makes it adaptable to dynamically changing websites.

Grid Infrastructure for Satellite Data Processing in Ukraine

In this paper conceptual foundations for the development of Grid systems that aimed for satellite data processing are discussed. The state of the art of development of such Grid systems is analyzed, and a model of Grid system for satellite data processing is proposed. An experience obtained within the development of the Grid system for satellite data processing in the Space Research Institute of NASU-NSAU is discussed.

Ontological Presentation of East-Christian Iconographical Art Domain

In the recent years the East-Christian iconographical art works have been digitized providing a large volume of data. The need for effective classification, indexing and retrieval of iconography repositories was the motivation of the design and development of a systemized ontological structure for description of iconographical art objects. This paper presents the ontology of the East-Christian iconographical art, developed to provide content annotation in the Virtual encyclopedia of Bulgarian iconography multimedia digital library. The ontology’s main classes, relations, facts, rules, and problems appearing during the design and development are described. The paper also presents an application of the ontology for learning analysis on an iconography domain implemented during the SINUS project “Semantic Technologies for Web Services and Technology Enhanced Learning”.