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Objective: Hepatitis E infection is one of the most frequent acute hepatitis in the world. Currently five human genotypes with different geographical distributions and distinct epidemiologic patterns are identified. In Slovakia, only rare cases of hepatitis E have been reported in recent years. Therefore, the aim of the study was to evaluate the prevalence of anti-HEV total antibodies and the main risk factors for HEV in the general population in Eastern Slovakia. Methods: Detection of anti-HEV total antibodies samples was done by a commercial enzyme-linked immunosorbent assay (ELISA) kit. Results: Of 175 hospitalized patients included in the study, 76 (43.5%) showed positivity for anti-HEV total antibodies. No statistically significant differences were found in anti-HEV positivity between men and women or in the groups of different living areas (town/village - urban/rural). Conclusion: Prevalence of anti-HEV total antibodies of hospitalised patients was high. The risk factor significantly associated with antibody positivity was eating raw meat. Other factors, such as sex, age, living area and contact with animals were not associated with antibody positivity.

Today, there are many parameters used for cardiovascular risk quantification and to identify many of the high-risk subjects, however, many of them do not reflect reality. Modern personalized medicine is the key to fast and effective diagnostics and treatment of cardiovascular diseases. One step towards this goal is a better understanding of connections between numerous risk factors. We used Factor analysis to identify a suitable number of factors on observed data about patients hospitalized in the East Slovak Institute of Cardiovascular Diseases in Košice. The data describes 808 participants cross-identifying symptomatic and coronarography resulting characteristics. We created several clusters of factors. The most significant cluster of factors identified six factors: basic characteristics of the patient, renal parameters and fibrinogen, family predisposition to CVD, personal history of CVD, lifestyle of the patient, and echo and ECG examination results. The factor analysis results confirmed the known findings and recommendations related to CVD. The derivation of new facts concerning the risk factors of CVD will be of interest to further research, focusing, among other things, on explanatory methods.

The aim of this article is to present how we processed and analysed data from a logistics company using various Business Intelligence tools. The theoretical part of the article is therefore focused on defining Business Intelligence concepts and data warehouses that are relevant to the issue. The practical part of the article focuses on editing data, creating dimension tables and facts. The data were collected from the Dynafleet system and originated from a shipping company. The data provided are for the period from 2013 to 2016. We design user scenarios to help the company's manager in making an efficient assignment of drivers to planned delivery routes. The research is focused on design and creation of a logistics system based on data analytics that can continuously analyze the incoming data and generate current decision support reports. The created user scenarios have a wide range of uses and can also be helpful in assessing the performance of individual drivers and their workloads. Using a logistics system, the logistics manager can get the valuable and useful information needed to effectively operate the business.

Data streams can be defined as the continuous stream of data in many forms coming from different sources. Data streams are usually non-stationary with continually changing their underlying structure. Solving of predictive or classification tasks on such data must consider this aspect. Traditional machine learning models applied on the drifting data may become invalid in the case when a concept change appears. To tackle this problem, we must utilize special adaptive learning models, which utilize various tools able to reflect the drifting data. One of the most popular groups of such methods are adaptive ensembles. This paper describes the work focused on the design and implementation of a novel adaptive ensemble learning model, which is based on the construction of a robust ensemble consisting of a heterogeneous set of its members. We used k-NN, Naive Bayes and Hoeffding trees as base learners and implemented an update mechanism, which considers dynamic class-weighting and Q statistics diversity calculation to ensure the diversity of the ensemble. The model was experimentally evaluated on the streaming datasets, and the effects of the diversity calculation were analyzed.

One of the most fundamental phenomena heavily influencing the digital society is Big Data. It is crucial not only to collect and analyze vast amounts of data but do it in an intelligent way. We believe that in order to do so, there needs to be a suitable interplay between the knowledge already known in the given application domain (background knowledge) and the knowledge inductively gained from data utilizing various data analysis techniques. We call it a knowledge-based approach to data analysis or intelligent data analysis. In this chapter, we will focus on two main types of the knowledge-based approach to data analysis. We start with the introduction of the semantic modelling of data analytics processes, which can efficiently cover an explicit form of background knowledge. The main focus here will be on the conceptualization of domain knowledge shared between the domain expert and data scientist and modelling of data mining workflows in order to achieve reproducibility and reusability. The second situation is typical for medical application, where the prevalent amount of background knowledge tends to stay tacit. In such a situation, the human-in-the-loop approach is a way how to perform data analysis intelligently. For both of these types of knowledge-based data analysis, specific case studies are presented to show how intelligent data analysis works in practice.

BACKGROUND Physical frailty, cognitive impairment, and symptoms of anxiety and depression frequently co-occur in later life, but, to date, each has been assessed separately. The present study assessed their patterns in primary care patients aged ≥60 years. MATERIAL AND METHODS This cross-sectional study evaluated 263 primary care patients aged ≥60 years in eastern Croatia in 2018. Physical frailty, cognitive impairment, anxiety and depression, were assessed using the Fried phenotypic model, the Mini-Mental State Examination (MMSE), the Geriatric Anxiety Scale (GAS), and the Geriatric Depression Scale (GDS), respectively. Patterns were identified by latent class analysis (LCA), Subjects were assorted by age, level of education, and domains of psychological and cognitive tests to determine clusters. RESULTS Subjects were assorted into four clusters: one cluster of relatively healthy individuals (61.22%), and three pathological clusters, consisting of subjects with mild cognitive impairment (23.95%), cognitive frailty (7.98%), and physical frailty (6.85%). A multivariate, multinomial logistic regression model found that the main determinants of the pathological clusters were increasing age and lower mnestic functions. Lower performance on mnestic tasks was found to significantly determine inclusion in the three pathological clusters. The non-mnestic function, attention, was specifically associated with cognitive impairment, whereas psychological symptoms of anxiety and dysphoria were associated with physical frailty. CONCLUSIONS Clustering of physical and cognitive performances, based on combinations of their grades of severity, may be superior to modelling of their respective entities, including the continuity and non-linearity of age-related accumulation of pathologic conditions.

Currently, just a few major parameters are used for cardiovascular (CV) risk quantification to identify many of the high-risk subjects; however, they leave a lot of them with an underestimated level of CV risk which does not reflect the reality. The submitted study design of the Kosice Selective Coronarography Multiple Risk (KSC MR) Study will use computer analysis of coronary angiography results of admitted patients along with broad patients’ characteristics based on questionnaires, physical findings, laboratory and many other examinations. Obtained data will undergo machine learning protocols with the aim of developing algorithms which will include all available parameters and accurately calculate the probability of coronary artery disease. The KSC MR study results, if positive, could establish a base for development of proper software for revealing high-risk patients, as well as patients with suggested positive coronary angiography findings, based on the principles of personalised medicine.

The medical procedures for disease diagnostics are significantly demanding and time-consuming. Data mining methods can accelerate this process and assist doctors in making decisions in complex situations. In case of Parkinson´s disease (PD), the diagnostics of the initial disease stage is the primary issue since the symptoms are not so unambiguous and easily observable. Therefore, this article is focused on determining the actual stage of PD based on the data recording signals of patient´s speech using decision trees (C4.5, C5.0 and CART). Methods such as RandomForest, Bagging and Boosting were also employed to improve the existing classification models. Estimation of model accuracy was achieved by using k-fold cross-validation and validation with omission of one record (Leave-one-out). In addition, experiments were also performed to remove collinearity in data by computing the Variance inflation factor (VIF) in order to increase the accuracy of the models.

Background There is potential for medical research on the basis of routine data used from general practice electronic health records (GP eHRs), even in areas where there is no common GP research platform. We present a case study on menopausal women with hypertension and metabolic syndrome (MS). The aims were to explore the appropriateness of the standard definition of MS to apply to this specific, narrowly defined population group and to improve recognition of women at high CV risk. Methods We investigated the possible uses offered by available data from GP eHRs, completed with patients interview, in goal of the study, using a combination of methods. For the sample of 202 hypertensive women, 47–59 years old, a data set was performed, consisted of a total number of 62 parameters, 50 parameters used from GP eHRs. It was analysed by using a mixture of methods: analysis of differences, cutoff values, graphical presentations, logistic regression and decision trees. Results The age range found to best match the emergency of MS was 51–55 years. Deviations from the definition of MS were identified: a larger cut-off value of the waist circumference measure (89 vs 80 cm) and parameters BMI and total serum cholesterol perform better as components of MS than the standard parameters waist circumference and HDL-cholesterol. The threshold value of BMI at which it is expected that most of hypertensive menopausal women have MS, was found to be 25.5. The other best means for recognision of women with MS include triglycerides above the threshold of 1.7 mmol/L and information on statins use. Prevention of CVD should focus on women with a new onset diabetes and comorbidities of a long-term hypertension with anxiety/depression. Conclusions The added value of this study goes beyond the current paradigm on MS. Results indicate characteristics of MS in a narrowly defined, specific population group. A comprehensive view has been enabled by using heterogenoeus data and a smart combination of various methods for data analysis. The paper shows the feasibility of this research approach in routine practice, to make use of data which would otherwise not be used for research.

(1) Objectives: We aimed to identify clusters of physical frailty and cognitive impairment in a population of older primary care patients and correlate these clusters with their associated comorbidities. (2) Methods: We used a latent class analysis (LCA) as the clustering technique to separate different stages of mild cognitive impairment (MCI) and physical frailty into clusters, the differences were assessed by using a multinomial logistic regression model. (3) Results: Four clusters (latent classes) were identified: (1) highly functional (the mean and SD of the “frailty” test 0.58 ± 0.72 and the Mini-Mental State Examination (MMSE) test 27.42 ± 1.5), (2) cognitive impairment (0.97 ± 0.78 and 21.94 ± 1.95), (3) cognitive frailty (3.48 ± 1.12 and 19.14 ± 2.30), and (4) physical frailty (3.61 ± 0.77 and 24.89 ± 1.81). (4) Discussion: The comorbidity patterns distinguishing the clusters depend on the degree of development of cardiometabolic disorders in combination with advancing age. The physical frailty phenotype is likely to exist separately from the cognitive frailty phenotype and includes common musculoskeletal diseases.

This paper reviews the Case-based reasoning (CBR) approach and its usability in the medicine and presents a new concept on how to improve its adaptation phase. We use the CBR as a supporting method for decision support like diseases diagnostics or therapy identification. We investigated existing approaches, studies, and research works to solve one of the most critical problems in the CBR cycle - adaptation, which is often done manually by the experts in the relevant field. Based on the findings and our experiences with medical diagnostics through suitable data analytical methods, we proposed a new solution to solve this challenge. This approach is based on a comparison of the stored decision rules with the new one related to the current case. This comparison can result in three alternative states: (1) case base contains a similar case, and relevant rule can be applied. (2) The new case is very different from the stored ones, so the input from participated experts is needed, and a new rule will be stored. (3) The new case is partially similar satisfying adaptability conditions, in such a situation we adopt related decision rule to the new conditions under the supervision of the expert. We plan to experimentally test and verify this concept within available medical samples from our previous experiments.

This article about Active learning in data science education deals with methods and tools to support active learning of data science, in particular within the Knowledge Discovery subject at the Technical University of Kosice, Slovakia. One of the results of the work presented in this article is an implemented web application to support active learning in data science. The article involves a brief overview of the methods supporting the active learning which may be used also for teaching data science. The final web application which was proposed and tested by the users, was created by RShiny application. The supporting application is helpful mainly for the students as an interactive mean of active learning in data science. The result of the article is available and it was applied in practice. This work contributes to the effective learning of the users (learners) as well as it contributes to better understanding, comprehension and application of the acquired knowledge and experiences of the students in practice by means of active learning.

The paper presents a new approach to effectively support the adaptation phases in the case-based reasoning (CBR) process. The use of the CBR approach in DSS (Decision Support Systems) can help the doctors better understand existing knowledge and make personalized decisions. CBR simulates human thinking by reusing previous solutions applied to past similar cases to solve new ones. The proposed method improves the most challenging part of the CBR process, the adaptation phase. It provides diagnostic suggestions together with explanations in the form of decision rules so that the physician can more easily decide on a new patient’s diagnosis. We experimentally tested and verified our semi-automatic adaptation method through medical data representing patients with cardiovascular disease. At first, the most appropriate diagnostics is presented to the doctor as the most relevant diagnostic paths, i.e., rules—extracted from a decision tree model. The generated rules are based on existing patient records available for the analysis. Next, the doctor can consider these results in two ways. If the selected diagnostic path entirely covers the actual new case, she can apply the proposed diagnostic path to diagnose the new case. Otherwise, our system automatically suggests the minimal rules’ modification alternatives to cover the new case. The doctor decides if the suggested modifications can be accepted or not.

Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches.

Metabolic syndrome (MS) represents an important risk factor for the development of cardiovascular diseases, as well as type 2 diabetes mellitus, which as one of a few clinical syndromes affects more than 25% of the world population. The diagnosis is often associated with various negative activities like little physical exercise, poor diet, stress, genetic predisposition, and excessive alcohol consumption. The aim of this paper is to provide a literature review of the current state of the art in the area of MS diagnosis by means of data mining methods. We structure our literature review by means of the CRISP-DM methodology, which is typically used to organize the analytical process. The reviewed problem was most often approached as a binary classification problem and frequently used methods have been decision trees, neural networks and logistic regression. Some of the authors applied also suitable statistical methods like Welch’s t-test, Pearson’s chi-squared test. Mostly, the size of analyzed data samples was more than one thousand patients.

The aim of this article is to describe the design, implementation and evaluation of the educational application to support learning of data mining algorithms. The role of the application is to help students to better understand the algorithms such as Naive Bayes classifier, decision trees and association rules. The application also includes a test area that allows students to generate and solve different types of tasks on one hand side and teachers provide an effective way to test students without the need for creating custom tests on the other side. Presented application was evaluated from the perspective of students and teachers of the subject Knowledge Discovery, in order to verify the functionality and usability of the application in the real teaching process.

Topic modeling becomes a popular research area which shows us new way to search, browse and summarize large amount of texts. Methods of topic modeling try to uncover the hidden thematic structure in document collections. Topic modeling in connection with social networks, which are one of the strongest communication tool and produces large amount of opinions and attitudes on world events, can be useful for analysis in case of crisis situations, elections, launching a new product on the market etc. For that reason we pro-pose a tool for topic modeling over text streams from social networks in this paper. Description of proposed tool is extended with practical experiments. Realized experiments shown promising results when using our tool on real data in comparison to state-of-the-art methods.

This article contains description how to create prediction models for more efficient utilization of running IT resources. Prediction model creation is presented as one of the most important steps within the process of the knowledge discovery. Presented models were created based on real data from a local IT company by means of the programming language R. This article also marginally covers the steps, which are to be carried out before the actual modelling. The steps to mention are the following: available data understanding and their subsequent preprocessing for further work, individual attributes analysis using data visualization by means of graphs and tracking certain behavior and correlations in data. First of all, designing models using linear and multiple regression will be followed in detail. Then, the methods of new values prediction will be described using the R language. Finally, the different types of prediction models will be considered using appropriate indicators. The results obtained should assist and simplify the methods of the knowledge discovery in certain enterprises based on the available databases, and also help to choose the most suitable prediction models learning method.

The paper describes a methodology for creating a simple modular warehouse system involving novel struc- tures and modifications of existing mobile frameworks. The novelty of the presented approach is the platform indepen- dence and simplicity of the architecture, simple prototype of a GUI and platform independent layer communication model. The proposed methodology is illustrated on the example of a real model application which was designed for a private com- pany. The system described and created is intended for use over Dynamics Navision ERP system. A lot of these topics may be considered challenging in the future research of business application development in general, either for desktop, web or mobile platforms. Since prospects in the field of mobile applications and mobile business solutions (m-business) are really promising for all leading PDA/tablet platforms, there is an expectation of a rising demand in the next years.

In this paper we present a proposal for a data-mining system deployed as a cloud service which is supposed to be used for a big data analysis. The main purpose of the system is the analysis of a vast number of event logs using means of data aggregation, clustering, classification and prediction. The system is composed of two components implemented as software services. The Automatic Predictor Generator is supposed to provide a meaningful way to aggregate large amounts of data and the Automatic Behavior Rule Extractor deals with proper analysis of these aggregations. Results of the system are the prediction rules usable for support of decision-making and in areas such as management, marketing, customer segmentation, classification, behavior prediction etc.

In this paper we describe the actual state of the big data analysis and related technologies teaching in Business information systems study program at the Technical University Kosice, Slovakia. We have been teaching at the Department of cybernetics and artificial intelligence, various courses specialized on data analysis, machine learning as well as knowledge discovery in databases. Advances and progress in technologies during recent years proved that there is a need to extend the teaching process by reflecting the new and emerging technologies, tools and approaches. During the last complex accreditation process we have proposed a new course specifically dedicated to the area of big data analysis and related technologies. The main objective of this paper is to summarize the motivation to include the newly introduced course within our curricula, as well as to describe the outline of the learning objectives, structure and content of the proposed course with focus on the design of the practical lab sessions.

Part 10: Big Data and Text Mining; International audience; The paper presents the Jbowl, Java software library for data and text analysis, and various research activities performed and implemented on top of the library. The paper describes the various analytical services for text and data mining implemented in Jbowl as well as numerous extensions aimed to address the evolving trends in data and text analysis and its usage in various tasks reflecting the areas such as big data analysis, distributed computing and parallelization. We also present the complex analytical platform built on top of the library, integrating the distributed computing analytical methods with the graphical user interface, visualization methods and resource management capabilities.

This paper analyses how information from user profile influences quality of recommendations. We first start with an overview of recommendation systems, their functions methods used. The empirical part focuses on collaborative filtering method with the aim to find improvement of recommendations based on the user profile. The main objective for realized experiments was to verify the hypothesis that using information stored in user's profiles can improve collaborative filtering recommendation results. All our experiments were realized in RapidMiner tool on well-known MovieLens dataset. For evaluation of results we used standard metrics such as RMSE, MAE and NMAE. Experiments did not confirm the above mentioned hypothesis, but we present additional analysis of identified clusters with best and worst recommendation results.

The aim of this chapter is to evaluate a potential of suitable data mining methods for analyses of aviation historical data. In our case, we used public aviation dataset from Federal Aviation Administration (FAA) Accident/Incident Data System containing information about civil aviation accidents or incidents within United States of America. This dataset represents interesting source of data for analytical purposes, e.g. it is possible to evaluate an influence of various factors on both types of events and based on identified hidden relations to generate a prediction model for specified target attribute. We compared our approach with some other existing works in this domain and obtained results are plausible and inspiring. Generated models could be used as a basis for aviation warning system or as a supporting method for different processes related to the aviation industry.

The work presented in this paper demonstrates how different data mining approaches can be applied to extend conventional combinations of variables determining the Metabolic Syndrome with new influential variables, which are easily available in the everyday physician‘s practice. The results have important consequences: patients with the Metabolic Syndrome can be recognized by using only some, one, or none of the conventional variables, when replaced with some other surrogate variables, available in patient health records, making diagnosis feasible in different work environments and at different time points of patient care. In addition, the results showed that there is a large diversity of patient groups, much larger than it was supposed earlier on when their identification was based on the conventional variables approach, indicating the underlying complexity of this syndrome. Finally, the discovered novel variables, indicating yet unknown pathogenetic pathways can be used to inspire future research.

Aspect-based sentiment analysis has become popular research field which allows the quantification of textual evaluations of different aspects of products and services. Methods of aspect-based sentiment analysis built on machine learning usually depend on manually annotated training corpora. In order to facilitate the processes of their creation, annotation tools dedicated to this purpose are needed. In this work we proposed a semi-automatic annotation tool which uses active learning to increase the effectiveness of the documents annotation. The use of active learning adapted to the needs of aspect-based sentiment analysis is the main difference between the proposed solution and existing annotation tools. We applied it in the domain of hotels evaluations. The results of realized experiments confirmed the faster increase of the annotation suggestions quality in terms of F1-measure in comparison to the scenario without active learning.

Business bankruptcy is a negative phenomenon, whose symptoms can be identified in advance by means of financial data analyses. The aim of this paper is to present two experimental studies using two different approaches to analyze company’s financial situation based on selected financial indicators. The first approach used data from financial database called Amadeus to generate a binary prediction model to evaluate a possible future financial health status of the EU companies using suitable machine learning algorithms. The second one included a design and creation of data warehouse based on data from two financial databases Albertina and Creditinfo (SK and CZ companies) to evaluate financial health status of the companies from Slovakia and Czech Republic through index of bankruptcy IN99.

This paper describes basic approaches for modeling collaboration social networks. Some of the new extensions are described – new approach for weighting of the ties among event participants; and temporal based method for modeling of network evolution – aging of the ties among actors in passing time. We evaluate proposed methods by two experiments with the real data set and we compare our modeled network with the reference data gathered from people’s opinions expressed in targeted inquiries.

The paper addresses the concept of Technology Enhanced Learning, which is in focus of the IT4KT project of the Technical University of Kosice. Namely, we present an approach towards the collaborative design and implementation of semantic structures that are capable to support an automation in selected educational processes, especially in mathematical and computer science disciplines. The resulting structure of created ontologies is described together with generic process models that enable a semantic composition and orchestration of electronic services in the analyzed courses. Added semantics allows a flexible integration of educational services and processes, which consequently may help educators to minimize their efforts when preparing and running new instances of courses or lectures, as well as to effectively analyze the students' performance and the teaching process as whole.

In this paper we present and compare experiences with two different approaches to the utilization of semantic technologies for the adaptive and personalized access to the services. By services we mean generally both web services accessible online and “traditional” services provided by the business or government organizations. We illustrate presented approaches based on our experiences from two projects, where applications in different areas, such as e-government, e-business and crisis management have been implemented. In these cases different approaches according to the used semantic services technology were applied.

Part 1: Cross-Domain Conference and Workshop on Multidisciplinary Research and Practice for Information Systems (CD-ARES 2013); International audience; Sentiment analysis is a popular research area devoted to methods allowing automatic analysis of the subjectivity in textual content. Many of these methods are based on the using of machine learning and they usually depend on manually annotated training corpora. However, the creation of corpora is a time-consuming task, which leads to necessity of methods facilitating this process. Methods of active learning, aimed at the selection of the most informative examples according to the given classification task, can be utilized in order to increase the effectiveness of the annotation. Currently it is a lack of systematical research devoted to the application of active learning in the creation of corpora for sentiment analysis. Hence, the aim of this work is to survey some of the active learning strategies applicable in annotation tools used in the context of sentiment analysis. We evaluated compared strategies on the domain of product reviews. The results of experiments confirmed the increase of the corpus quality in terms of higher classification accuracy achieved on the test set for most of the evaluated strategies (more than 20% higher accuracy in comparison to the random strategy).

E-learning represents still dynamically evolving area of research. For one of the sources of its persistent topicality can be considered its close relationship to the development in the fields of web services, semantic web, online collaboration, and many others, that is constantly reflected by e-learning solutions. The view of e-learning solutions, in which they were perceived as an independent solutions designed specifically to support educational processes, is changing, too. It is becoming increasingly common to integrate them into the wider context of Web portals of institutions and to make their content publicly accessible. A similar intent led us to design and implement the Web portal of Knowledge discovery course, which is taught at the Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics at the Technical University of Kosice. Our intention was to create a solution to support educational processes, which would also represent a publicly available knowledge base dedicated to the topic of knowledge discovery, as part of the virtual laboratory of Business information systems. In this paper we describe the implementation details of the proposed solution built on the results of four ongoing projects. The solution is described both in terms of technological platform, based on the integration of existing open source solutions and our own extensions as well as in terms of the specifics of its content, which is based on video tutorials, collaboratively created documents and case studies.

In this paper we report on a study of 1541 articles from three different journals (Journal of Informetrics, Information Processing and Management, and Computers and Electrical Engineering) from the period 2007–2014. We analyzed their dates of submission and of final decision to accept and investigated whether the difference between these two dates (the so-called “time to accept”) is smaller for the articles authored by the corresponding journal’s editorial board members and whether longer times to accept yield higher citation counts. The main results are that we found significantly shorter times to accept editorial board member’s articles only in Journal of Informetrics and not in the other two journals, and that articles in any of these journals that took longer to be accepted did not receive markedly more citations.

This paper deals with social network analysis targeted to decomposition and local structure analysis of organization to organization network in order to get view of local structure characteristics in this network.

Technology enhanced learning is one of the intensively researched domains, because it promises interesting contribution to modern educational processes at various levels, in particular in higher education. This paper presents our approach to design and implementation of semantic structures that are capable to support selected educational processes, e.g. in mathematical and computer science disciplines. The methodology for collaborative ontology development, applied on the analyzed domain of eleven courses, is proposed as a generic approach that can be potentially reused and adapted to create semantic models of other domains (courses, disciplines) for semantically supported technology enhanced learning applications. The resulting structure of developed ontologies is presented as a basis for constructing semantically enhanced electronic services that can be further orchestrated and integrated into complex educational processes. This approach may provide powerful means to educators at universities, namely for (semi- )automated preparation, running, and maintenance of their courses. Added semantics, by enabling an integration of learning services into specified educational processes, is capable to minimize the efforts of educators when preparing and running a new lecture or course, as well as to effectively analyze the whole process and the students' performance.

Analysing and reflecting on one’s own and other’s working practices is an essential meta-activity for any kind of project work, but also plays a prominent role in the type of object-oriented inquiry and trialogical learning the KP-Lab project is focusing on. Analysis and reflection, therefore, are not understood just as means to optimize or improve a given way of working but also as an active and productive process, geared towards the advancement of knowledge practices.

This paper proposes a client warehouse application over Microsoft Dynamics Navision 5.0 enterprise resource planning system. The application has been created to meet criteria and constraints stated by real world company, or in terms of application itself, the future user. By means of effectiveness, created application shortens or eliminates some warehouse activities. Employees can easily roam in warehouse and create physical inventory, transfer, intake or expend goods using PDAs with WiFi connection to database server. Furthermore, if WiFi signal is not strong enough to cover the whole working area of the warehouse, there is a possibility to synchronize essential data between source server database and local device compact database. Usage of barcode scanner makes every operation even more effective in terms of activity time consumption.

Sentiment analysis deals with methods for automatic analysis of the subjective aspects of the text. In this contribution we first present an overview of main approaches currently used in sentiment analysis. We further focus on feature selection methods for sentiment analysis and propose a new approach to feature selection. Our approach has been experimentally evaluated on movie review dataset. The results show that the proposed method is computationally efficient and in exchange sacrifices only a small amount of accuracy.

In this paper we analyze basic approaches for modeling of collaboration networks and propose some new extensions in weighting ties among event participants. One of the extensions is based on the idea of aging of the ties among collaborators. Classical as well as newly proposed approaches to weighting of ties in collaboration networks are experimentally evaluated. We performed two experiments with real data set and as a reference we used data gathered from peoples' opinions expressed in targeted inquiries.

This paper presents a short-term prediction of fog occurrence based on suitable data mining methods. The whole process was implemented through CRISP-DM methodology that represents most commonly used approach for data mining. This methodology consists of six main phases, which we describe in this paper for our application: business understanding, data understanding, data preparation, modeling, evaluation and deployment that resulted into new and useful knowledge to be used in real practice. The main motivation behind our solution was to develop an effective data mining model based on local conditions at the airport for short-term fog prediction as crucial factor for air management. Our first results presented in this paper are promising.

This paper describes design and implementation aspects for extension of our original software system developed in Java for support of information retrieval and text mining with specialized execution engine for different type of tasks. Some of our experiences and specific requirements of the real applications lead us to idea give the system possibility to run the tasks in distributive way. The result of the idea is task-based execution engine, which represents middleware-like transparent layer (mostly for programmers who want to re-use functionality of our package) for running of different tasks in multi-thread environment. The original system is being developed as open source with the intention to provide an easy extensible, modular framework for pre-processing, indexing and further exploration of large text collections. This specialized execution engine has been implemented within APVV project called PoZnaŤ (Support for knowledge creation processes) in order to extend proposed text-mining platform for educational and experimental purposes. Conceptual architecture of the system is provided as well as details regarding our extension of library like usage of content repository paradigm, representation and encapsulation of tasks, implementation of the engine itself and its role within PoZnaŤ platform.

This paper presents a particular approach how to support the knowledge creation processes that are based on innovative idea of trialogical learning.The key task in trialogical learning is to understand how the “weaker” forms of knowledge are used and made explicit in a meaningful way in collaborative processes. Trialogical learning provides mechanism by which tacit knowledge is conceptualized in collaborative systems. Within the "Knowledge-Practices Laboratory” (KP-Lab) project suitable tools and practices for support of trialogical learning are being designed, implemented and evaluated. The KP-LAB platform offers, except of others, also facilities for discovering, managing and visualization of some tacit knowledge. We present in this paper one particular approach how to this is supported with proposed awareness features

This paper deals with the role of ontologies in collaborative systems. The research field of collaborative systems and their support by information and communication technologies describes theory called CSCW (computer supported cooperative work). During last few years the CSCW has been evolving and extending, e.g. CSCL (computer supported collaborative learning) has emerged. With a well-defined ontology structure, CSCL can accumulate the knowledge representation of learning objects, including participant background, group information, instruction designs, learning activities and learning outcomes.

The Webocracy project aims to empower citizens with innovative communication, access and polling system, supporting increased participation in democratic processes. One of the main project goals is to develop an open source web-based system called Webocrat as a multi-channel communication platform supporting e-government and e-democracy applications. Some of the Webocrat modules were tested in first trial of our two pilot applications, one of them running in Wolverhampton, UK and the other one in Kosice, Slovakia. Currently, whole integrated Webocrat system is being tested within the second trials.

There is a growing number of e-Government portals and solutions available today. But what the users lack in particular is a customised assistance - help that meets the individual situation and competence [13]. In this paper, a system called Webocrat will be presented as an attempt to shift e-Government portals toward this direction, providing knowledge management strategy as its basis [11]. The Webocrat system applies a knowledge-based approach [5]. Information of all kinds produced by various modules is linked to a shared ontology representing an application domain. Such ontology serves as a means for structuring and organizing available information resulting in improved search capability and contents presentation.

Objectives and experiences gained during the first period of the IST project Webocracy (IST-1999-20364 "Web Technologies Supporting Direct Participation in Democratic Processes") are described in the paper. Project Webocracy contributes to empowering citizens with innovative communication; an access and polling system supporting increased participation of citizens in democratic processes and increased transparency and accessibility of public administration. A user-driven approach is applied within the project. Therefore, at this initial stage of the project, user requirements have been identified and based on them. The Webocracy system architecture is being refined.

It has become very important for advanced organisations to make the best use of information gathered from various document sources inside companies and from external sources like the Internet. There are many technologies under development, which address knowledge discovery. On the other hand, there is a lack of efficient technologies focused on organising and sharing of existing knowledge. In this paper we introduce the research in scope of Know Webi (EC funded project). We focus our attention on two important issues — (i) how to capture tacit, contextual knowledge that is connected to the documents and (ii) how to support knowledge management in geographically distributed organisations through up-to-date communication and AI technologies

A new scheduling agent for existing CIM multi-agent system is being currently developed at the Technical University of Kosice. The basic idea is to create an agent based on Scheduling Program developed at the Technical University of Kosice. The Scheduling Program was developed in the ECL i PS e programming language using its constraint logic programming capabilities. It is used for solving Job-Shop problems. This paper gives the reader some basic information about prepared software.