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Background: High prevalence and mortality of cardiovascular diseases (CVD) are global problems. Many countries focus their healthcare on secondary treatment, and primary prevention is in the background. Focusing on risk factors (RF) screening of CVD in personalized medicine has great potential for the future. We present a methodology for identification of important RF as well as the potentially new once. Materials and Methods: We worked with the dataset of patients hospitalized in the East Slovak Institute of Cardiovascular Diseases in Košice. The file contained 808 records, complete history, laboratory tests, ECG, echocardiography, and selective coronary angiography. We analyzed the importance of variables based on CART, Random forest, and Logistic regression algorithms (binary classification) and propose a new weighted agglomerative attribute importance metric. After selection of potentially important, but less known RF we re-deployed the CART algorithm on selected combinations of risk factors, while the target attribute was divided into six original classes corresponding to the severity of the coronarography finding.Results: Selected important variables based on the proposed metric are in accordance with known results, but also pointed to some potentially relevant RF such as fibrinogen. The experiments confirmed that fibrinogen might have the potential to help determine cardiovascular risk. However, its impact is debatable, but its potential increases if it is potentiated by factors other than HDL. We also concluded that higher HDL levels might have a cardio protective effect. Conclusions: Our study proposed original methodology for identification of important RF and in depth analysis of potentially interesting new RF. Results showed that fibrinogen could be one of the critical risk factors of cardiovascular diseases. Experimental results suggest that there may be others besides traditional RF of CVD, such as the fibrinogen and HDL levels we investigate. Still, studies with a larger patient population are needed to draw significant conclusions.

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.

While digital space is a place where users communicate increasingly, the recent threat of COVID-19 infection even more emphasised the necessity of effective and well-organised online environment. Therefore, it is nowadays, more whenever in the past, important to deal with various unhealthy phenomena, that prohibit effective communication and knowledge sharing in the digital space. Undesired user behaviour and user-generated content in the online environment (mostly on social media) can have various forms, probably, the most harmful is the creation and spreading of false information (e.g., fake news) and using abusive language (e.g. hate speech). While a notable amount of research effort has been already dedicated to reducing and mitigating the negative consequences of such phenomena, a number of additional challenges and open problems remain unsolved. In this book chapter, at first, we provide a summary of existing research works, challenges and open problems. Consequently, we introduce our research results addressing false information and abusive language. Our approaches are based on intelligent and knowledge-based methods, mainly machine learning, natural language processing, and semi-automatic approaches. Besides the detection of undesired content, we present also less studied approaches to identify users, who contribute or spread such content. Our approaches are also complemented by the methods that are specific for computational social science and humanities as hyperlink network analysis, conceptual analysis, and interviews. Finally, we present some of our applications for monitoring and mitigating such undesired content and behaviour.

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.

In this paper we present a decision support system, which has been designed and implemented on the case-based reasoning principles. Our decision support system is being implemented in tight cooperation with the cardiologist, who represents the main future users of the system. Our system enables its user to find the most similar historical cases to a new patient, suggest the most probable result of the potential coronary angiography examination and also provide various useful visualizations to the cardiologist, who is responsible for the final decision about recommending the coronary angiography or not for the new patient. The first response from the cardiologist about our system is very promising.

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.

This article presents a contribution to the general public education about the risks which people are facing on the web and how to prevent them. We first briefly characterize and present a typology of antisocial behavior on the web, analyze and compare some of the existing tools. Next, we present the design and implementation of a website to inform internet users about the possibilities of detecting antisocial behavior. A user study showed that the web application fulfills its purpose and is well accepted by the users.

(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.

E-mail marketing is one of the main channels of communication with existing and potential customers. The Open Rate metric is as one of the primary indicators of email campaign success. There are many features of e-mail communication affecting the behavior of individual recipients. Understanding and properly setting these features imply the success of email marketing campaigns. One of these features is the time to send the e-mail. In this paper, we present a methodology for predicting suitable email sending time. Analyzing the available data collected from e-mail communications between companies and customers creates a space for applying data mining methods to data. The proposed methodology for the generation of prediction models to determine the optimal time to send an email has been implemented and evaluated on a real dataset with very promising results.

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.

Human urine is one of most accessible body fluids and its collection does not burden patients. Wide spectrum of compounds present in urine with a close relation to metabolism determines a large diagnostic potential. Presented work is focused on information extraction from autofluorescent data analysis of urine. Simple modified synchronous autofluorescent spectrum of urine sample – human fluorescent profile of urine (HFPU) seems to be powerful in mining distinct characters and could be meaningful tool for classification of patients into subgroups according to specific fluorescent characteristics without exact knowledge of urine composition. This could be beneficial in broad range of applications on urine autofluorescence. Offered concept of autofluorescent fingerprints evaluation was tested on 119 urine samples. In this study was found that patients with hypertension have lower intensity of fluorescence at the wavelength range 380–450 nm compare to healthy.

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.

This paper approaches the problem of processing medical records with the aim of data analysis from a larger set of domain specific medical data. We solve this problem in cooperation with doctors from Department of Cardiology of the East Slovak Institute of Cardiovascular Disease. For this purpose, we designed, implemented and tested first prototype of a software system able to read and process medical records about cardiological patients and provide anonymized data for further analysis.

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.

Data analytics represents a new chance for medical diagnosis and treatment to make it more effective and successful. This expectation is not so easy to achieve as it may look like at a first glance. The medical experts, doctors or general practitioners have their own vocabulary, they use specific terms and type of speaking. On the other side, data analysts have to understand the task and to select the right algorithms. The applicability of the results depends on the effectiveness of the interactions between those two worlds. This paper presents our experiences with various medical data samples in form of SWOT analysis. We identified the most important input attributes for the target diagnosis or extracted decision rules and analysed their interestingness with cooperating doctors, for most promising new cut-off values or an investigation of possible important relations hidden in data sample. In general, this type of knowledge can be used for clinical decision support, but it has to be evaluated on different samples, conditions and ideally in long-term studies. Sometimes, the interaction needed much more time than we expected at the beginning but our experiences are mostly positive.

The heart disease describes a range of conditions affecting our heart. It can include blood vessel diseases such as coronary artery disease, heart rhythm problems or and heart defects. This term is often used for cardiovascular disease, i.e. narrowed or blocked blood vessels leading to a heart attack, chest pain or stroke. In our work, we analysed three available data sets: Heart Disease Database, South African Heart Disease and Z-Alizadeh Sani Dataset. For this purpose, we focused on two directions: a predictive analysis based on Decision Trees, Naive Bayes, Support Vector Machine and Neural Networks; descriptive analysis based on association and decision rules. Our results are plausible, in some cases comparable or better as in other related works.

The aim of this paper is to apply predictive data mining (DM) techniques in order to predict the average fuel consumption for trucks and drivers resp., to identify the key factors that affect fuel consumption of vehicles and also to identify best practices and driving styles of drivers. For this purpose different models have been proposed to provide an overview of the key factors affecting fuel consumption for individual vehicles and their drivers. Predictive models enabled us to identify main influencing factors and provide recommendations for a logistics company to reduce the fuel consumption. Data were collected from Dynafleet information system of a small transport company. The company is dealing with freight traffic, particularly trucks. We first describe selected projects dealing with similar tasks in this area. Next, we explore and analyze data using CRISP-DM methodology by appropriate methods designed for data mining and then evaluate the results of the experiments.

Medical diagnostic is a complex process consisting of many input variables, which the general practitioner (GP) or specialist should take into account before confirm the expected diagnosis. In the case of electronic records, they have an opportunity to support this process within simple understandable results of the correctly applied suitable methods from machine learning or statistics. We used a small sample of patient's data from Croatia for experimental evaluation of this potential. We applied the methods as Welch's t-test, Pearson chi-square independence test, Youden's index, decision trees and simple K-Means. The cooperating medical expert evaluated the obtained results and confirmed the expected potential for daily medical practice.

Selection of the right decision strategy is a crucial factor to success in the foreign exchange market. This article presents an innovative approach how to support related decision steps by means of suitable data mining methods applied on collected data from the market. The motivation is a trading under the best conditions, i.e. with the highest chance to be successful. To meet this requirement, we designed and implemented a decision support system (DSS) for trading on the foreign exchange market which uses a possibility to speculate on this market and in line with extracted rules, economic news and outputs of the technical analysis recommend the future trading direction. We extracted the rules from the historical Forex data with the C5.0 and CART algorithms for decision trees generation. The best achieved accuracy was 56.03% that is typical for this type of data. We used the best rules to design a dynamic trading strategy, which we experimentally verified as profitable.

Parkinson's disease is the second most frequent neurodegenerative disorder after Alzheimer's disease. There are numerous symptoms among the population suffering from the disease including tremor, slowed movement, impaired posture and balance, and rigid muscles, however dysphonia — changes in speech and articulation — is the most significant precursor. This is the reason why the article is focused on patients classification based on their speech signals. Algorithms C4.5, C5.0, RandomForest and CART were used to generate the decision trees in Rstudio interface. In addition, cut-off values of individual attributes were applied in order to classify the patients. The dataset in the article consists of 40 individuals' records, half of which were affected by Parkinson's disease. Each individual's coverage was represented by several records such as permanent vowels pronunciation, certain words, numbers and sentences. The objective was to determine the most accurate classification model (decision tree) using the individual types of speech signals. Cross-validation method was used for evaluation of models. The highest average model accuracy of 66.5% was obtained for data taken when individuals pronounced the numbers.

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.

This paper deals with social network analysis targeted to local structure analysis of company network in order to get view of local structure characteristics and patterns in such kind of network. This paper describes an original approach, which has been experimentally verified on Slovak company network. We first aimed to prove presence of significant local structures in Slovak company network and then we use original technique “local structure pattern mining” to identify common local structures (patterns) in this network.

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.