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Big Data in technologies from IBM
Šoltýs, Matej ; Novotný, Ota (advisor) ; Hrabina, Pavel (referee)
This diploma thesis presents Big Data technologies and their possible use cases and applications. Theoretical part is initially focused on definition of term Big Data and afterwards is focused on Big Data technology, particularly on Hadoop framework. There are described principles of Hadoop, such as distributed storage and data processing, and its individual components. Furthermore are presented the largest vendors of Big Data technologies. At the end of this part of the thesis are described possible use cases of Big Data technologies and also some case studies. The practical part describes implementation of demo example of Big Data technologies and it is divided into two chapters. The first chapter of the practical part deals with conceptual design of demo example, used products and architecture of the solution. Afterwards, implementation of the demo example is described in the second chapter, from preparation of demo environment to creation of applications. Goals of this thesis are description and characteristics of Big Data, presentation of the largest vendors and their Big Data products, description of possible use cases of Big Data technologies and especially implementation of demo example in Big Data tools from IBM.
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Innovative trends of Business Intelligence and Big Data in the model of Design-driven Innovation
Krčma, Marek ; Pour, Jan (advisor) ; Derfler, Václav (referee)
Business Intelligence plays the crucial role in the question of serching for the truth in organizations. Trend of data growing defines the importance of analytical tools for organizations. Innovation is perceived as the only driver which leads to higher living standards in a society in the longterm run (according to the World Economic Forum). This thesis joins two areas: innovation and analytical field of business informatics (Business Intelligence, Big Data). The main goal of this thesis is to identify innovation trends of Business Intelligence and Big Data and to classify them using the Design-driven Innovation model. The thesis also provides a broad perspective of the innovation process in organizations and reveals the mutuality of innovation process, productivity and competitiveness.
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The utilisation of social network data in BI
Linhart, Ondřej ; Pavlíček, Antonín (advisor) ; Jaroš, Vladimír (referee)
The thesis deals with the topic of social networks, particularly with the opportunities the utilisation of social network data can provide to an enterprise. The thesis is divided into two parts: The theoretical part contains definitions of the terms of data, information and knowledge, followed by descriptions of Business Intelligence and Big Data -- the two means of data analysis in an enterprise, and later by describing social networks themselves. The practical part contains an analysis of the data provided by social networks Facebook and Twitter, and at the same time defines the process of data extraction. The outcome of the analysis is a set of data that may possibly be obtained by the enterprise. This data is then used to determine the possible ways in which enterprises can leverage the data for their business. Finally data provided by Czech e shop is used to provide an example of how an entity can utilise social network data.
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Prediktívna analytika v retailovom bankovníctve v Českej republike
Búza, Ján ; Král, Petr (advisor) ; Tyl, Ondřej (referee)
Advanced analytics and big data allow a more complete picture of customers' preferences and demands. Through this deeper understanding, organizations of all types are finding new ways to engage with existing or potential customers. Research shows that companies using big data and advanced analytics in their operations have productivity and profitability rates that are 5 to 6 percent higher compared to their peers. At the same time it is almost impossible to find a banking institution in the Czech Republic exploiting potential of data analytics to its full extent. This thesis will therefore focus on exploring opportunities for banks applicable in the local context, taking into account technological and financial limitations as well as the market situation. Author will conduct interviews with bank managers and management consultants familiar with the topic in order to evaluate theoretical concepts and the best practices from around the world from the point of Czech market environment, to assess capability of local banks to exploit them and identify the main obstacles that stand in the way. Based on that a general framework for bank managers, who would like to use advanced analytics, will be proposed.
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Cloud computing and its introduction in small and medium sized enterprises
Šimonfy, Adam
Simonfy, A. Cloud computing and its introduction in small and medium enterpris-es. Bachelor thesis. Brno: Mendel University in Brno, 2015. This thesis is compiled on the Cloud computing and its introduction in the small and medium enterprises and evaluation of its connection to the up-coming tech-nologies and chosen factors connected to the operations management with the stress on costs. Cloud computing shows strong benefits for the business. The study of selected model company showed that it is justified for a company to con-sider the technology, however, it might not be applicable for all kinds of small and medium enterprises.
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Robust Regularized Cluster Analysis for High-Dimensional Data
Kalina, Jan ; Vlčková, Katarína
This paper presents new approaches to the hierarchical agglomerative cluster analysis for high-dimensional data. First, we propose a regularized version of the hierarchical cluster analysis for categorical data with a large number of categories. It exploits a regularized version of various test statistics of homogeneity in contingency tables as the measure of distance between two clusters. Further, our aim is cluster analysis of continuous data with a large number of variables. Various regularization techniques tailor-made for high-dimensional data have been proposed, which have however turned out to suffer from a high sensitivity to the presence of outlying measurements in the data. As a robust solution, we recommend to combine two newly proposed methods, namely a regularized version of robust principal component analysis and a regularized Mahalanobis distance, which is based on an asymptotically optimal regularization of the covariance matrix. We bring arguments in favor of the newly proposed methods.
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High-Performance Analytics (HPA)
Soukup, Petr ; Pour, Jan (advisor) ; Novotný, Ota (referee)
The aim of the thesis on the topic of High-Performance Analytics is to gain a structured overview of solutions of high performance methods for data analysis. The thesis introduction concerns with definitions of primary and secondary data analysis, and with the primary systems which are not appropriate for analytical data analysis. The usage of mobile devices, modern information technologies and other factors caused a rapid change of the character of data. The major part of this thesis is devoted particularly to the historical turn in the new approaches towards analytical data analysis, which was caused by Big Data, a very frequent term these days. Towards the end of the thesis there are discussed the system sources which greatly participate in the new approaches to the analytical data analysis as well as in the technological solutions of High Performance Analytics themselves. The second, practical part of the thesis is aimed at a comparison of the performance in conventional methods for data analysis and in one of the high performance methods of High Performance Analytics (more precisely, with In-Memory Analytics). Comparison of individual solutions is performed in identical environment of High Performance Analytics server. The methods are applied to a certain sample whose volume is increased after every round of executed measurement. The conclusion evaluates the tests results and discusses the possibility of usage of the individual High Performance Analytics methods.
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Big Data and its perspective for the banking
Firsov, Vitaly ; Maryška, Miloš (advisor) ; Molnár, Zdeněk (referee)
In this thesis, I want to explore present (y. 2012/2013) modern trends in Business Intelligence and focus specifically on the rapidly evolving and, in my (and not only) opinion, a very perspective area of analysis and use of Big Data in large enterprises. The first, introductory part contains general information and the formal conditions as aims of the work, on whom the work is oriented and where it could be used. Then there are described inputs and outputs, structure, methods to achieve the objectives, potential benefits and limitations in this part. Because at the same time I work as a data analyst in the largest bank Czech Republic, Czech Savings Bank, I focused on the using of Big Data in the banking, because I think, that it is possible to achieve great benefits from collecting and analyzing Big Data in this area. The thesis itself is divided into 3 parts (chapters 2, 3-4, 5). In the second chapter you will learn, how developed the area of BI, how it evolved historically, what is BI today and what future is predicted to the BI by the experts like the world famous and respected analyst firm Gartner. In the third chapter I will focus on Big Data itself, what this term means, how Big Data differs from traditional business information available from ERP, ECM, DMS and other enterprise systems. You will learn about ways to store and process this type of data, as well as about the existing and applicable technologies, focused on Big Data analysis. In the fourth chapter I focus on the using of Big Data in business, information in this chapter will reflect my personal views on the potential of Big Data, based on my experience during practice in Czech Savings Bank. The final part will summarize this thesis, assess, how I fulfilled the objectives defined at the beginning, and express my opinion on perspective of the trend of Big Data analytics, based to the analyzed during the writing this thesis information and knowledge.
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Eight ICT trends that will change libraries
Černý, Michal
Informační společnost i rychle se rozvíjející ICT proměňují vše kolem nás – od vzdělávání, přes dopravu až třeba právě po knihovny. Příspěvek představí osm technologií, které do deseti let začnou měnit knihovny téměř k nepoznání: Internet věcí; big data; veřejné multimediální displeje; firemní sociální sítě; cloud; nové mobilní sítě; zpracování přirozeného jazyka či sémantické technologie. Co bude tato změna znamenat pro knihovny? Jak se změní jejich postavení v informační společnosti?
Video:  MP4
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The influence of trends in BI project
Kapitán, Lukáš ; Pour, Jan (advisor) ; Rezek, Tomáš (referee)
The aim of this these is to analyse the trends occurring in Business intelligence. It does examine, summarise and judge each of the trends from the point of their usability in the real world, their influence and modification of each phase of the implementation of Bussiness intelligence. It is clear that each of these trends has its positives and negatives which can influence the statements in the evaluation. These factors are taken into consideration and analysed as well. The advantages and disadvantages of the trends are occurring especially in the areas of economical demand and technical difficultness. The main aim is to compare the methods of implementation of Bussiness intelligence with actual trends in BI. In order to achieve this a few crucial points were set: to investigate recent trends in the BI and to define the methods of implementation in the broadest terms. The awaited benefit of this these is already mentioned investigation and analysis of trends in the area of Bussiness intelligence and its use in implementation.
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