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Optimization of the Distributed I/O Subsystem of the k-Wave Project
Vysocký, Ondřej ; Hrbáček, Radek (referee) ; Jaroš, Jiří (advisor)
This thesis deals with an effective solution of parallel writing of variable amounts of data on the Lustre file system. The work will be used by the k-Wave project designed for time domain acoustic and ultrasound simulations. Since the simulation is computationally and data intensive, the project requires to be implemented with libraries for parallel computig (Open MPI) and large data processing (HDF5) and it must run on a supercomputer. The application is implemented in C and uses previously mentioned libraries. The proper settings of the Lustre file system leads to the peak write bandwith of 2.5 GB/s that corresponds to a speedup factor of 5 compared to the reference settings. The data aggregation improved the write bandwidth by a factor of 3 compared to a naive version. Here, the achieved I/O bandwidth for certain block sizes hits the limits of the Anselm I/O subsytem (3GB/s).
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BigData Approach to Management of Large Netflow Datasets
Melkes, Miloslav ; Ráb, Jaroslav (referee) ; Ryšavý, Ondřej (advisor)
This master‘s thesis focuses on distributed processing of big data from network communication. It begins with exploring network communication based on TCP/IP model with focus on data units on each layer, which is necessary to process during analyzation. In terms of the actual processing of big data is described programming model MapReduce, architecture of Apache Hadoop technology and it‘s usage for processing network flows on computer cluster. Second part of this thesis deals with design and following implementation of the application for processing network flows from network communication. In this part are discussed main and problematic parts from the actual implementation. After that this thesis ends with a comparison with available applications for network analysis and evaluation set of tests which confirmed linear growth of acceleration.
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Information System Assessment and Proposal for ICT Modification
Križanský, Ján ; Klusák, Aleš (referee) ; Koch, Miloš (advisor)
This thesis focuses on an assessment of the current information system in a small company followed by a plan proposal for its replacement. Main goal is to compare multiple different approaches to the process of implementing a new information system in a company and the use of methodical approach for their comparison. The result of this should be a recommendation to whether the information system should be replaced and if so, what will be the resulting gain for the company.
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Comparison of machine learning methods for credit risk analysis
Bušo, Bohumír ; Kolman, Marek (advisor) ; Vacek, Vladislav (referee)
Recently, machine learning has been put into connection with a field called ,,Big Data'' more and more. Usually, in this field, a lot of data is available and we need to gather useful information based on this data. Nowadays, when still more and more data is generated by use of mobile phones, credit cards, etc., a need for high-performance methods is serious. In this work, we describe six different methods that serve this purpose. These are logistic regression, neural networks and deep neural networks, bagging, boosting and stacking. Last three methods compose a group called Ensemble Learning. We apply all six methods on real data, which were generously provided by one of the loan providers. These methods can help them to distinguish between good and bad potential takers of loans, when the decision about the loan is being made. Lastly, the results of particular methods are compared and we also briefly outline possible ways of interpretation.
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Hadoop and Business Intelligence
Kerner, Josef ; Šperková, Lucie (advisor) ; Augustín, Jakub (referee)
The main purpose of this thesis is to describe how an integration of a Hadoop platform into currently existing Business Intelligence technologies and processes can augment its data processing and analysis capabilities while encountering Big Data. Furthermore, it describes reasons why the whole Hadoop application ecosystem was founded and informs the reader about the functionality of its primary components. It continues with provision of overview about Hadoop higher-level components architecture and their use in existing Business Intelligence processes such as data ingestion, transformation and analysis. In the last theoretical chapter it focuses itself on describing specific areas of utilization of the Hadoop platform and Big Data in data warehousing, text mining and predictive analytics. From the practical point of view, a particular use case is provided, an implementation of Big Data ETL process in the field of financial markets and trading with a detailed explanation of the corresponding necessities such as data model, ETL code and proposed metrics, which can be further implemented for achieving increased return on investments.
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ICT trends in China and Taiwan
Kačala, Dominik ; Samec, Marek (advisor) ; Potančok, Martin (referee)
This bachelor thesis deals with the actual trends in the area of information and communication technologies, focusing on Far East countries with emphasis on China and Taiwan. The theoretical foundation of this work is to provide an overview of the current situation in the ICT market and its subsectors in individual countries. The specifics of countries in relation to information technology are also briefly described. The second part focuses on the actual analysis of selected ICT trends in China and Taiwan and compares specific approach to them from different angles. In the last part of the thesis conclusions and results of the analysis from the second chapter are applied in comparison with the West, USA and EU specifically and examined trends are evaluated in the global context. The contribution of this paper is to provide a comprehensive overview of the area of selected ICT trends in the countries concerned.
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Application of Big Data in the banking environment
Dvorský, Bohuslav ; Chlapek, Dušan (advisor) ; Palovská, Helena (referee)
This thesis addresses the principles and technologies of Big Data and their usage in the banking environment. Its objective is to find business application scenarios for Big Data for purposes of delivering added value for the bank. Finding the scenarios have been achieved by studying literature and consultation with experts, they were also subsequently modeled by the author. Possibilities of application of these scenarios in the banking busi-ness environment were subsequently verified by the survey, which interviewed profession-als on issues relating to the found business scenarios. The thesis first explains the basic con-cepts and approaches of Big Data, the status of this technology compared to traditional technologies and issues of integration into the banking environment. After this theoretical beginning the business scenarios are found and modeled followed by the exploration and evaluation. Selected business scenarios are further verified for the purpose of determining the suitability or unsuitability for implementation using technologies and principles of Big Data. The contribution of this work is to find a real use of Big Data in banking, where most of the materials on this topic is very general and vague. This thesis verifies two business scenarios that can big a bank institution high added value if they are implemented with Big Data platform.
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Big Data analysis in healthcare
Nováková, Martina ; Kučera, Jan (advisor) ; Chlapek, Dušan (referee)
This thesis deals with the analysis of Big Data in healthcare. The aim is to define the term Big Data, to acquaint the reader with data growth in the world and in the health sector. Another objective is to explain the concept of a data expert and to define team members of the data experts team. In following chapters phases of the Big Data analysis according to methodology of EMC2 company are defined and basic technologies for analysing Big Data are described. As beneficial and interesting I consider the part dealing with definition of tasks in which Big Data technologies are already used in healthcare. In the practical part I perform the Big Data analysis task focusing on meteorotropic diseases in which I use real medical and meteorological data. The reader is not only acquainted with the one of recommended methods of analysis and with used statistical models, but also with terms from the field of biometeorology and healthcare. An integral part of the analysis is also information about its limitations, the consultation on results, and conclusions of experts in meteorology and healthcare.
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New Trends in Business Intelligence - Focus on Big Data and Hadoop
Korkisch, Josef ; Vacek, Martin (advisor) ; Pour, Jan (referee)
The thesis is focused on several specific areas. The first area is the identification of new trends in the field of Business Intelligence in recent years. Furthermore, the thesis focuses on Big Data (Hadoop). The main purpose is to show the possible use of the Hadoop technology on real-world examples. What is also important, is a section dedicated to comparison of Business Intelligence tools for working with Big Data as it is the section which proofs standpoint from several significant aspects -- angels (analysis, visualization and connectivity). The work is divided into several parts in which the components of Business Intelligence, new trends in BI, Hadoop technology in the context of BI, Hadoop components are described, as well as examples of using Hadoop and comparison of selected BI tools to work with BI is introduced within the thesis. The main purpose of the thesis is to serve as a guide to the world of BI and Big Data (Hadoop).
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