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Dr. Martin Sarnovsky
Technical University of Kosice, Department of Cybernetics and Artifcial Intelligence

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Research Keywords & Expertise

0 Big Data
0 Data Analytics
0 IT Service Management
0 Knowledge Discovery
0 Machine Learning

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Machine Learning
Big Data
Data Analytics
IT Service Management
Knowledge Discovery

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Short Biography

Martin Sarnovsky is an assistant professor at the Department of Cybernetics and Artificial Intelligence at Faculty of Electrical Engineering and Computer Science, Technical University in Košice. In 2009, he received his PhD. degree in the Artificial Intelligence. He has experience of working on multiple national and international research projects. His scientific research is mostly focused on data and text analysis, also including the Big Data and data streams analysis and processing. His other fields of professional interests include semantic technologies and knowledge modelling using ontologies, and IT service management.

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Journal article
Published: 01 April 2021 in PeerJ Computer Science
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Data streams can be defined as the continuous stream of data coming from different sources and in different forms. Streams are often very dynamic, and its underlying structure usually changes over time, which may result to a phenomenon called concept drift. When solving predictive problems using the streaming data, traditional machine learning models trained on historical data may become invalid when such changes occur. Adaptive models equipped with mechanisms to reflect the changes in the data proved to be suitable to handle drifting streams. Adaptive ensemble models represent a popular group of these methods used in classification of drifting data streams. In this paper, we present the heterogeneous adaptive ensemble model for the data streams classification, which utilizes the dynamic class weighting scheme and a mechanism to maintain the diversity of the ensemble members. Our main objective was to design a model consisting of a heterogeneous group of base learners (Naive Bayes, k-NN, Decision trees), with adaptive mechanism which besides the performance of the members also takes into an account the diversity of the ensemble. The model was experimentally evaluated on both real-world and synthetic datasets. We compared the presented model with other existing adaptive ensemble methods, both from the perspective of predictive performance and computational resource requirements.

ACS Style

Martin Sarnovsky; Michal Kolarik. Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble. PeerJ Computer Science 2021, 7, e459 .

AMA Style

Martin Sarnovsky, Michal Kolarik. Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble. PeerJ Computer Science. 2021; 7 ():e459.

Chicago/Turabian Style

Martin Sarnovsky; Michal Kolarik. 2021. "Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble." PeerJ Computer Science 7, no. : e459.

Conference paper
Published: 21 January 2021 in 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)
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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.

ACS Style

Michal Kolarik; Martin Sarnovsky; Jan Paralic. Diversity in Ensemble Model for Classification of Data Streams with Concept Drift. 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) 2021, 000355 -000360.

AMA Style

Michal Kolarik, Martin Sarnovsky, Jan Paralic. Diversity in Ensemble Model for Classification of Data Streams with Concept Drift. 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI). 2021; ():000355-000360.

Chicago/Turabian Style

Michal Kolarik; Martin Sarnovsky; Jan Paralic. 2021. "Diversity in Ensemble Model for Classification of Data Streams with Concept Drift." 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) , no. : 000355-000360.

Journal article
Published: 01 January 2021 in Acta Polytechnica Hungarica
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ACS Style

Martin Sarnovsky; Jan Marcinko. Adaptive Bagging Methods for Classification of Data Streams with Concept Drift. Acta Polytechnica Hungarica 2021, 18, 47 -63.

AMA Style

Martin Sarnovsky, Jan Marcinko. Adaptive Bagging Methods for Classification of Data Streams with Concept Drift. Acta Polytechnica Hungarica. 2021; 18 (3):47-63.

Chicago/Turabian Style

Martin Sarnovsky; Jan Marcinko. 2021. "Adaptive Bagging Methods for Classification of Data Streams with Concept Drift." Acta Polytechnica Hungarica 18, no. 3: 47-63.

Conference paper
Published: 22 December 2020 in Advances in Intelligent Systems and Computing
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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.

ACS Style

Peter Bednár; Ján Paralič; František Babič; Martin Sarnovský. Knowledge-Based Approaches to Intelligent Data Analysis. Advances in Intelligent Systems and Computing 2020, 75 -97.

AMA Style

Peter Bednár, Ján Paralič, František Babič, Martin Sarnovský. Knowledge-Based Approaches to Intelligent Data Analysis. Advances in Intelligent Systems and Computing. 2020; ():75-97.

Chicago/Turabian Style

Peter Bednár; Ján Paralič; František Babič; Martin Sarnovský. 2020. "Knowledge-Based Approaches to Intelligent Data Analysis." Advances in Intelligent Systems and Computing , no. : 75-97.

Journal article
Published: 02 December 2020 in Applied Sciences
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The emergence of anti-social behaviour in online environments presents a serious issue in today’s society. Automatic detection and identification of such behaviour are becoming increasingly important. Modern machine learning and natural language processing methods can provide effective tools to detect different types of anti-social behaviour from the pieces of text. In this work, we present a comparison of various deep learning models used to identify the toxic comments in the Internet discussions. Our main goal was to explore the effect of the data preparation on the model performance. As we worked with the assumption that the use of traditional pre-processing methods may lead to the loss of characteristic traits, specific for toxic content, we compared several popular deep learning and transformer language models. We aimed to analyze the influence of different pre-processing techniques and text representations including standard TF-IDF, pre-trained word embeddings and also explored currently popular transformer models. Experiments were performed on the dataset from the Kaggle Toxic Comment Classification competition, and the best performing model was compared with the similar approaches using standard metrics used in data analysis.

ACS Style

Viera Maslej-Krešňáková; Martin Sarnovský; Peter Butka; Kristína Machová. Comparison of Deep Learning Models and Various Text Pre-Processing Techniques for the Toxic Comments Classification. Applied Sciences 2020, 10, 8631 .

AMA Style

Viera Maslej-Krešňáková, Martin Sarnovský, Peter Butka, Kristína Machová. Comparison of Deep Learning Models and Various Text Pre-Processing Techniques for the Toxic Comments Classification. Applied Sciences. 2020; 10 (23):8631.

Chicago/Turabian Style

Viera Maslej-Krešňáková; Martin Sarnovský; Peter Butka; Kristína Machová. 2020. "Comparison of Deep Learning Models and Various Text Pre-Processing Techniques for the Toxic Comments Classification." Applied Sciences 10, no. 23: 8631.

Conference paper
Published: 12 November 2020 in 2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA)
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Fake news detection currently presents an active field of research. Detection methods based on natural language processing and machine learning are being developed to automatically identify the possible misinformation contained within the news articles. To successfully train these models, annotated data are needed. In English language, multiple human-annotated datasets already are available and are being widely used in the research. The main objective of the work presented in this paper, was to create similar dataset consisting of articles in Slovak language. We collected the data from the various local news portals including reputable publishers as well as suspicious conspiratory portals. To obtain the annotations, we used crowdsourcing approach. Annotated dataset was used in preliminary experiments, in which neural network classifier was trained and evaluated.

ACS Style

Martin Sarnovsky; Viera Maslej-Kresnakova; Nikola Hrabovska. Annotated dataset for the fake news classification in Slovak language. 2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA) 2020, 574 -579.

AMA Style

Martin Sarnovsky, Viera Maslej-Kresnakova, Nikola Hrabovska. Annotated dataset for the fake news classification in Slovak language. 2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA). 2020; ():574-579.

Chicago/Turabian Style

Martin Sarnovsky; Viera Maslej-Kresnakova; Nikola Hrabovska. 2020. "Annotated dataset for the fake news classification in Slovak language." 2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA) , no. : 574-579.

Journal article
Published: 23 October 2020 in Electronics
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This paper connects two large research areas, namely sentiment analysis and human–robot interaction. Emotion analysis, as a subfield of sentiment analysis, explores text data and, based on the characteristics of the text and generally known emotional models, evaluates what emotion is presented in it. The analysis of emotions in the human–robot interaction aims to evaluate the emotional state of the human being and on this basis to decide how the robot should adapt its behavior to the human being. There are several approaches and algorithms to detect emotions in the text data. We decided to apply a combined method of dictionary approach with machine learning algorithms. As a result of the ambiguity and subjectivity of labeling emotions, it was possible to assign more than one emotion to a sentence; thus, we were dealing with a multi-label problem. Based on the overview of the problem, we performed experiments with the Naive Bayes, Support Vector Machine and Neural Network classifiers. Results obtained from classification were subsequently used in human–robot experiments. Despise the lower accuracy of emotion classification, we proved the importance of expressing emotion gestures based on the words we speak.

ACS Style

Martina Szabóová; Martin Sarnovský; Viera Maslej Krešňáková; Kristína Machová. Emotion Analysis in Human–Robot Interaction. Electronics 2020, 9, 1761 .

AMA Style

Martina Szabóová, Martin Sarnovský, Viera Maslej Krešňáková, Kristína Machová. Emotion Analysis in Human–Robot Interaction. Electronics. 2020; 9 (11):1761.

Chicago/Turabian Style

Martina Szabóová; Martin Sarnovský; Viera Maslej Krešňáková; Kristína Machová. 2020. "Emotion Analysis in Human–Robot Interaction." Electronics 9, no. 11: 1761.

Journal article
Published: 01 February 2020 in Symmetry
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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.

ACS Style

Martin Sarnovsky; Jan Paralic. Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model. Symmetry 2020, 12, 203 .

AMA Style

Martin Sarnovsky, Jan Paralic. Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model. Symmetry. 2020; 12 (2):203.

Chicago/Turabian Style

Martin Sarnovsky; Jan Paralic. 2020. "Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model." Symmetry 12, no. 2: 203.

Conference paper
Published: 01 November 2019 in 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA)
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ACS Style

Martin Sarnovsky; P. Bednar. Challenges of Big Data Technologies in Education Process in Business Information Systems. 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA) 2019, 1 .

AMA Style

Martin Sarnovsky, P. Bednar. Challenges of Big Data Technologies in Education Process in Business Information Systems. 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA). 2019; ():1.

Chicago/Turabian Style

Martin Sarnovsky; P. Bednar. 2019. "Challenges of Big Data Technologies in Education Process in Business Information Systems." 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA) , no. : 1.

Conference paper
Published: 01 November 2019 in 2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics (CINTI-MACRo)
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Spreading of misinformation on the web nowadays represents a serious issue, as their influence on peoples opinions may be significant. Fake news represents a specific type of misinformation. While its detection was mostly being performed manually in the past, automated methods using machine learning and related fields became more critical. On the other hand, deep learning methods became very popular and frequently used methods in the field of data analysis in recent years. The study presented in this paper deals with the detection of fake news from the textual data using deep learning techniques. Our main idea was to train different types of neural network models using both entire texts from the articles and to use just the title text. The models were trained and evaluated on the Fake News dataset obtained from the Kaggle competition.

ACS Style

Viera Maslej Kresnakova; Martin Sarnovsky; Peter Butka. Deep learning methods for Fake News detection. 2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics (CINTI-MACRo) 2019, 000143 -000148.

AMA Style

Viera Maslej Kresnakova, Martin Sarnovsky, Peter Butka. Deep learning methods for Fake News detection. 2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics (CINTI-MACRo). 2019; ():000143-000148.

Chicago/Turabian Style

Viera Maslej Kresnakova; Martin Sarnovsky; Peter Butka. 2019. "Deep learning methods for Fake News detection." 2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics (CINTI-MACRo) , no. : 000143-000148.

Journal article
Published: 13 May 2019 in Processes
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The process industries rely on various software systems and use a wide range of technologies. Predictive modeling techniques are often applied to data obtained from these systems to build the predictive functions used to optimize the production processes. Therefore, there is a need to provide a proper representation of knowledge and data and to improve the communication between the data scientists who develop the predictive functions and domain experts who possess the expert knowledge of the domain. This can be achieved by developing a semantic model that focuses on cross-sectorial aspects rather than concepts for specific industries, and that specifies the meta-classes for the formal description of these specific concepts. This model should cover the most important areas including modeling the production processes, data analysis methods, and evaluation using the performance indicators. In this paper, our primary objective was to introduce the specifications of the Cross-sectorial domain model and to present a set of tools that support data analysts and domain experts in the creation of process models and predictive functions. The model and the tools were used to design a knowledge base that could support the development of predictive functions in the green anode production in the aluminum production domain.

ACS Style

Martin Sarnovsky; Peter Bednar; Miroslav Smatana. Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries. Processes 2019, 7, 281 .

AMA Style

Martin Sarnovsky, Peter Bednar, Miroslav Smatana. Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries. Processes. 2019; 7 (5):281.

Chicago/Turabian Style

Martin Sarnovsky; Peter Bednar; Miroslav Smatana. 2019. "Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries." Processes 7, no. 5: 281.

Journal article
Published: 18 March 2019 in Informatics
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Distributed computing technologies allow a wide variety of tasks that use large amounts of data to be solved. Various paradigms and technologies are already widely used, but many of them are lacking when it comes to the optimization of resource usage. The aim of this paper is to present the optimization methods used to increase the efficiency of distributed implementations of a text-mining model utilizing information about the text-mining task extracted from the data and information about the current state of the distributed environment obtained from a computational node, and to improve the distribution of the task on the distributed infrastructure. Two optimization solutions are developed and implemented, both based on the prediction of the expected task duration on the existing infrastructure. The solutions are experimentally evaluated in a scenario where a distributed tree-based multi-label classifier is built based on two standard text data collections.

ACS Style

Martin Sarnovsky; Marek Olejnik. Improvement in the Efficiency of a Distributed Multi-Label Text Classification Algorithm Using Infrastructure and Task-Related Data. Informatics 2019, 6, 12 .

AMA Style

Martin Sarnovsky, Marek Olejnik. Improvement in the Efficiency of a Distributed Multi-Label Text Classification Algorithm Using Infrastructure and Task-Related Data. Informatics. 2019; 6 (1):12.

Chicago/Turabian Style

Martin Sarnovsky; Marek Olejnik. 2019. "Improvement in the Efficiency of a Distributed Multi-Label Text Classification Algorithm Using Infrastructure and Task-Related Data." Informatics 6, no. 1: 12.

Conference paper
Published: 01 August 2018 in 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA)
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Despite excessive amount of research done in the field of automated RTS gameplay, subtle changes in strategies are often ignored leading to non-optimal results. Researchers often consider obvious strategical decisions in order to cover as many gameplay scenarios as possible. In this paper, we focus on creating a dataset of atypical strategies which are often overlooked and using machine learning methods for detection of these strategies during the gameplay. Such information could be used to predict the strategies before they occur, or to correspond which adaptive behavior is able to answer them. In work presented in this paper, we approached the strategy recognition in StarCraft game using a set of classifiers trained on data obtained from the various replays. As there was not a proper dataset available to solve such task during our work, the dataset was created and annotated to cover four selected strategies for our experiments. Binary classification models were trained to detect each particular strategy and evaluated in a set of replay data using cross-validation technique. Then the overall platform architecture to train the models, export them and use in runtime during the gameplay was designed. Best performed models were then applied to real games to detect the covered strategies in replays or matches by bots or human players.

ACS Style

Martin Certicky; Martin Sarnovsky; Tomas Varga. Use of Machine Learning Techniques in Real-Time Strategy Games. 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) 2018, 159 -164.

AMA Style

Martin Certicky, Martin Sarnovsky, Tomas Varga. Use of Machine Learning Techniques in Real-Time Strategy Games. 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA). 2018; ():159-164.

Chicago/Turabian Style

Martin Certicky; Martin Sarnovsky; Tomas Varga. 2018. "Use of Machine Learning Techniques in Real-Time Strategy Games." 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) , no. : 159-164.

Journal article
Published: 01 March 2018 in Acta Electrotechnica et Informatica
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ACS Style

Martin Sarnovsky; Juraj Surma. PREDICTIVE MODELS FOR SUPPORT OF INCIDENT MANAGEMENT PROCESS IN IT SERVICE MANAGEMENT. Acta Electrotechnica et Informatica 2018, 18, 57 -62.

AMA Style

Martin Sarnovsky, Juraj Surma. PREDICTIVE MODELS FOR SUPPORT OF INCIDENT MANAGEMENT PROCESS IN IT SERVICE MANAGEMENT. Acta Electrotechnica et Informatica. 2018; 18 (1):57-62.

Chicago/Turabian Style

Martin Sarnovsky; Juraj Surma. 2018. "PREDICTIVE MODELS FOR SUPPORT OF INCIDENT MANAGEMENT PROCESS IN IT SERVICE MANAGEMENT." Acta Electrotechnica et Informatica 18, no. 1: 57-62.

Journal article
Published: 01 February 2018 in 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI)
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Process industries represent a significant share of the European industry in terms of energy consumption and environmental impact. In this area, data analytics techniques can prove effective when applied to the optimization of production processes and can lead to significant savings, both economic and environmental. However, application of these techniques is not straightforward. In many cases, process industries must invest in the monitoring and data integration as well as in the development and maintenance of the underlying infrastructure for data analytics, often capable of handling big data. In this talk, we present the architecture of a cross-sectorial Big Data platform for the process industries. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of large volumes of data from multiple industry domains. Such platform should be able to connect to the existing environment in the plant and use the data gathered to build predictive functions to optimize the production processes. The analytical platform will contain a development environment enabling the data scientists to build these functions and a simulation environment to evaluate the models. The platform will be shared among multiple sites from different industry sectors. Cross-sectorial sharing will enable the transfer of knowledge across different domains. The deployed architecture was tested in two process industry domains, one from the aluminium production and the other from the plastic moulding area.

ACS Style

Martin Sarnovsky. Big data processing and analytics for process industries. 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI) 2018, 1 .

AMA Style

Martin Sarnovsky. Big data processing and analytics for process industries. 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI). 2018; ():1.

Chicago/Turabian Style

Martin Sarnovsky. 2018. "Big data processing and analytics for process industries." 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI) , no. : 1.

Journal article
Published: 26 January 2018 in Big Data and Cognitive Computing
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This paper describes the architecture of a cross-sectorial Big Data platform for the process industry domain. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of data from multiple industry domains. Such a platform should be able to connect to the existing environment in the plant and use the data gathered to build predictive functions to optimize the production processes. The analytical platform will contain a development environment with which to build these functions, and a simulation environment to evaluate the models. The platform will be shared among multiple sites from different industry sectors. Cross-sectorial sharing will enable the transfer of knowledge across different domains. During the development, we adopted a user-centered approach to gather requirements from different stakeholders which were used to design architectural models from different viewpoints, from contextual to deployment. The deployed architecture was tested in two process industry domains, one from the aluminium production and the other from the plastic molding industry.

ACS Style

Martin Sarnovsky; Peter Bednar; Miroslav Smatana. Big Data Processing and Analytics Platform Architecture for Process Industry Factories. Big Data and Cognitive Computing 2018, 2, 3 .

AMA Style

Martin Sarnovsky, Peter Bednar, Miroslav Smatana. Big Data Processing and Analytics Platform Architecture for Process Industry Factories. Big Data and Cognitive Computing. 2018; 2 (1):3.

Chicago/Turabian Style

Martin Sarnovsky; Peter Bednar; Miroslav Smatana. 2018. "Big Data Processing and Analytics Platform Architecture for Process Industry Factories." Big Data and Cognitive Computing 2, no. 1: 3.

Conference paper
Published: 01 October 2017 in 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)
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The main objective of work presented in this paper is to introduce the architectural overview of the big data analytics platform for support of process industries. Our aim was to design and develop the cross-sectorial scalable environment, which will enable the data collection from different sources and support the development of predictive functions to help the process industries in optimizing of their production processes. This paper introduces the components of Big Data Storage and Analytics platform which is the core component of the developed cross-sectorial environment. Currently, it is built on top of the Apache Hadoop technology stack and relies on Hadoop distributed file system. On the other hand, we present the idea of integration of the data obtained from different production environments. Data integration is implemented using the Apache Nifi and we designed the workflows for processing both interval and real-time data from the production sites. In this case, we consider two pilot cases, an aluminium factory in France and a plastic molding factory in Portugal.

ACS Style

Martin Sarnovsky; P. Bednar; M. Smatana. Data integration in scalable data analytics platform for process industries. 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) 2017, 000187 -000192.

AMA Style

Martin Sarnovsky, P. Bednar, M. Smatana. Data integration in scalable data analytics platform for process industries. 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). 2017; ():000187-000192.

Chicago/Turabian Style

Martin Sarnovsky; P. Bednar; M. Smatana. 2017. "Data integration in scalable data analytics platform for process industries." 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) , no. : 000187-000192.

Conference paper
Published: 01 September 2017 in Advances in Intelligent Systems and Computing
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Nowadays, business process management and process modeling is an important part of business management and business process improvement. Resulting from the increasingly fast growing customer requirements for the final product, organizations need to introduce process management. The work presented in this paper was situated in the area of the automotive industry, which is the most important sector of the world economy. Technical development is progressing with rapid fashion, and even in the automotive industry, there is a need to monitor, manage and improve production processes. This is related to the deployment and use of information systems that support and automate these processes. The main goal of the work described in the paper is to provide a brief overview of the field of business process management, process modeling, related technologies, and the situation of the automotive industry in general. This work also analyzes the current situation in one of the measurement centers, selected processes, and user requirements for the application supporting those processes. Based on the analysis, we proposed an improvement of the selected processes and also designed a web application that supports these processes. The application was built using current technologies such as ASP.NET Core Framework, MVC architecture and the available libraries. Results of testing proved that the solution meets all requirements and in terms of key performance indicators surpassed the expected results. It is suitable for real use in practice and in the future, it is recommended its deployment to all measurement centers.

ACS Style

Martin Sarnovsky; Petra Cibulova. Measurement Center Processes Support in the Automotive Industry. Advances in Intelligent Systems and Computing 2017, 295 -303.

AMA Style

Martin Sarnovsky, Petra Cibulova. Measurement Center Processes Support in the Automotive Industry. Advances in Intelligent Systems and Computing. 2017; ():295-303.

Chicago/Turabian Style

Martin Sarnovsky; Petra Cibulova. 2017. "Measurement Center Processes Support in the Automotive Industry." Advances in Intelligent Systems and Computing , no. : 295-303.

Proceedings article
Published: 20 March 2017 in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)
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The main objective of the work presented within this paper was to design and implement the system for twitter data analysis and visualization in R environment using the big data processing technologies. Our focus was to leverage existing big data processing frameworks with its storage and computational capabilities to support the analytical functions implemented in R language. We decided to build the backend on top of the Apache Hadoop framework including the Hadoop HDFS as a distributed filesystem and MapReduce as a distributed computation paradigm. RHadoop packages were then used to connect the R environment to the processing layer and to design and implement the analytical functions in a distributed manner. Visualizations were implemented on top of the solution as a RShiny application.

ACS Style

Martin Sarnovsky; Peter Butka; Andrea Huzvarova. Twitter data analysis and visualizations using the R language on top of the Hadoop platform. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) 2017, 327 -332.

AMA Style

Martin Sarnovsky, Peter Butka, Andrea Huzvarova. Twitter data analysis and visualizations using the R language on top of the Hadoop platform. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). 2017; ():327-332.

Chicago/Turabian Style

Martin Sarnovsky; Peter Butka; Andrea Huzvarova. 2017. "Twitter data analysis and visualizations using the R language on top of the Hadoop platform." 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) , no. : 327-332.

Proceedings article
Published: 20 March 2017 in 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)
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Presented paper describes the use of clustering methods in building environment analysis task. The presented approach is based on modeling of the sensor data containing information about humidity and temperature. Such models are then used to describe the level of the comfort of particular environment. K-means clustering algorithm was used to create those models. The paper then presents and describes a method of user interaction with the environment model. User feed-back represents how the user feels in the current environment. Feedback is then collected and evaluated. Based on the feedback, models can trigger the change of current environment or during the time, re-compute themselves in order to pro-vide more precise building environment representation. Our solution was based on real sensor data obtained from university buildings and presented solution was implemented on top of Hadoop cluster using Mahout library for machine learning.

ACS Style

Martin Sarnovsky; David Bajus. Building environment analysis based on clustering methods from sensor data on top of the Hadoop platform. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) 2017, 79 -82.

AMA Style

Martin Sarnovsky, David Bajus. Building environment analysis based on clustering methods from sensor data on top of the Hadoop platform. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). 2017; ():79-82.

Chicago/Turabian Style

Martin Sarnovsky; David Bajus. 2017. "Building environment analysis based on clustering methods from sensor data on top of the Hadoop platform." 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) , no. : 79-82.