Diploma thesis is focused on the area of corporate performance management in corporation. As the time passes and technology is developing, the amount of information is still increasing. The company has to work with these information and process data based on which it can make decisions in everyday business. It is crucial for the company to know how to process data and create processes and information systems so the data can be used for effective decisions making process. The goal of this thesis is to introduce corporate performance management as the complex system and make the proposal of the concept for the company on Slovak and Czech market trading with information technology. Thesis is divided into two parts. The first one is about introducing principles of corporate performance management (as abbreviation CPM) -- introduction of components, history and also the place of CPM in architecture of the company. It also describes the possible future of importance in the company IT structure. In the end of first part there are introduced 5 principles of integrating CPM into real company life cycle. In the second part, thesis is about application of CPM into the company on Czech and Slovak market. In the first place, analysis was done about the state of each of components of CPM. Afterwards, all components of CPM were again designed so it would be possible to reach the best results. In the second part there is also detailed manual according to 5 principles by Palladino how to implement corporate performance management into this company.

The thesis focuses mainly on OLAP technology and its tools using to analyze corporate data. One of the objectives is to define the analytical tools market and provide an overview which shows the current situation concerning those tools on Business Intelligence (BI) market. Another objective is to obtain an overview of some current and future trends in the BI market. The main purpose of the thesis is to create a "methodology" for solving an BI analytical tasks using analytical services in SQL Server 2008 R2 from Microsoft. To achieve these objectives a theoretical analysis of available resources and, in particular consultation with employees of Clever Decision company is used. The main contribution of this work is the development of analytical database itself, which is divided into separate phases where each phase contains best practices and recommendations, including examples.

Business Intelligence (BI) has now become a core part to many businesses due to the benefits it brings -- the ability to access data in a common format from multiple sources and making decisions that help organization to move closer to its goals. Many workers today spend most of their time away from their office and can't easily access corporate data. The use of the Internet on mobile devices has changed the way people consume information. Embracing mobile computing means having the ability to access information anywhere, anytime and as a result consumers can make decisions faster. Thesis is focused on mobile Business Intelligence and the aim is to provide o comprehensive overview of this area, examine market trends, define critical success factors for implementation projects, and introduce mobile BI platform QlikView. Furthermore it explains the differences between traditional and mobile BI, explores benefits and drawbacks of these tools and the challenges that must be addressed. The reader will learn what types of employees and tasks are suited for mobile BI programs and outlined critical success factors might be valuable information for any company planning implementation of mobile BI tools.

This diploma thesis concerns the posibility of application Balanced Scorecard in production company management by using Business Intelligence tools. The goals of the thesis is definic strategic goals using Balanced Scorecard method, creation of the metrics from strategic goals and design and implementation of BI application to watt key metrics. The esential concepts concerning Business Intelligence and Balanced Scorecard are defined in the theoretical part of the thesis. Esential principals of BI and basic perspectives of BSC are defined there. Effects and risks of applying Business Intelligence are discussed in the thesis. Strategic goals are defined in the practical part of the thesis. A strategic map is created from the strategic goals in the thesis. Defined strategic goals are used for the creation of the metrics that are used to create the indicators. The indicators are created in the part of the thesis where BI is implemented. Only some metrics were chosen for the implementation of BI. Sorce data is transformed and stored in the multidimensional diabase. These data and indicators are used to create OLAP cube. Then the reports are created from data in this cube. Outputs of the thesis are BSC analysis including strategic map of the goals, BI implementation using MS SQL Server and reports using MS Excel and MS ProClarity.

Since the concept of business intelligence (BI) was introduced in the late 1980s, many organizations have implemented BI to improve performance but not all BI initiatives have been successful. Practitioners and academicians have discussed the reasons for success and failure, yet, a consistent picture about how to achieve BI success has not yet emerged. The purpose of this dissertation is to help fill the gap in research and provide a better understanding of BI success by examining the impact of BI capabilities on BI success, in the presence of different decision environments. The decision environment is a composition of the decision types and the way the required information is processed to aid in decision making. BI capabilities are defined as critical functionalities that help an organization improve its performance, and they are examined in terms of organizational and technological capabilities. An online survey is used to obtain the data and partial least squares path modeling (PLS) is used for analysis. The results of this dissertation suggest that all technological capabilities as well as one of the organizational capabilities, flexibility, significantly impact BI success. Results also indicate that the moderating effect of decision environment is significant for quantitative data quality. These findings provide richer insight in the role of the decision environment in BI success and a framework with which future research on the relationship between BI capabilities and BI success can be conducted. Findings may also contribute to practice by presenting information for managers and users of BI to consider about their decision environment in assessing BI success.

Análise de agrupamento é um problema fundamental de aprendizado de máquina não supervisionado em que se objetiva determinar categorias que descrevam um conjunto de objetos de acordo com suas similaridades ou inter-relacionamentos. Na formulação tradicional do problema, busca-se por partições ou hierarquias de partições contendo grupos cujos objetos são de alguma forma similares entre si e dissimilares aos objetos dos demais grupos, segundo alguma medida direta ou indireta de (dis)similaridade que leva em conta o conjunto completo de atributos que descrevem os objetos na base de dados sob análise. Entretanto, apesar de décadas de aplicações bem sucedidas, existem situações em que a natureza dos agrupamentos contidos nos dados não pode ser representada segundo este tipo de formulação. Em particular, existem situações em que grupos de objetos se caracterizam como tais apenas segundo um subconjunto dos atributos que os descrevem, sendo que tal subconjunto pode ser distinto para cada grupo. Ao contrário de algoritmos de agrupamento tradicionais, algoritmos de bi-agrupamento são capazes de agrupar simultaneamente linhas e colunas de uma matriz de dados. Tais algoritmos produzem bi-grupos formados por subconjuntos de objetos e subconjuntos de atributos de alguma forma fortemente co-relacionados. Esses algoritmos passaram a atrair a atenção da comunidade científica quando se evidenciou a relevância da tarefa de bi-agrupamento em problemas de análise de dados de expressão gênica em bioinformática. Embora em menor grau, as abordagens de bi-agrupamento também têm ganho atenção em outros domínios de aplicação, tais como mineração de textos (text mining) e filtragem colaborativa em sistemas de recomendação. O problema é que uma variedade de algoritmos de bi-agrupamento têm sido propostos na literatura baseados em diferentes princípios e suposições sobre os dados, podendo chegar a resultados completamente distintos em uma mesma aplicação. Nesse cenário, torna-se importante a realização de estudos comparativos que possam contrastar o comportamento e desempenho dos diversos algoritmos. Neste trabalho é apresentado um estudo comparativo envolvendo 17 algoritmos de bi-agrupamento (representativos das principais categorias de algoritmos existentes) em coleções de bases de dados tanto de natureza real como simulada, com particular ênfase em problemas de análise de dados de expressão gênica. Diversos aspectos metodológicos e procedimentos para a avaliação experimental foram considerados, a fim de superar as limitações de estudos comparativos anteriores da literatura. Além da comparação em si, todo o arcabouço comparativo pode ser reutilizado para a comparação de outros algoritmos no futuro. / Data clustering is a fundamental problem in the unsupervised machine learning field, whose objective is to find categories that describe a dataset according to similarities between its objects. In its traditional formulation, we search for partitions or hierarchies of partitions containing clusters such that the objects contained in the same cluster are similar to each other and dissimilar to objects from other clusters according to a similarity or dissimilarity measure that uses all the data attributes in its calculation. So, it is supposed that all clusters are characterized in the same feature space. However, there are several applications where the clusters are characterized only in a subset of the attributes, which could be different from one cluster to another. Different than traditional data clustering algorithms, biclustering algorithms are able to cluster the rows and columns of a data matrix simultaneously, producing biclusters formed with strongly related subsets of objects and subsets of attributes. These algorithms started to draw the scientific communitys attention only after some studies that show their importance for gene expression data analysis. To a lesser degree, biclustering techniques have also been used in other application domains, such as text mining and collaborative filtering in recommendation systems. The problem is that several biclustering algorithms have been proposed in the past recent years with different principles and assumptions, which could result in different outcomes in the same dataset. So, it becomes important to perform comparative studies that could illustrate the behavior and performance of some algorithms. In this thesis, it is presented a comparative study with 17 biclustering algorithms (which are representative of the main categories of algorithms in the literature) which were tested on synthetic and real data collections, with particular emphasis on gene expression data analysis. Several methodologies and experimental evaluation procedures were taken into account during the research, in order to overcome the limitations of previous comparative studies from the literature. Beyond the presented comparison, the comparative methodology developed could be reused to compare other algorithms in the future.

The increasing digitization makes digital systems more and more complex within the processing industry. This poses a challenge for organizations to obtain a long-term, functioning relationship between the information system in use and its users. A phenomenon linked to this challenge is that of user resistance, which is an acknowledged critical factor regarding information systems failure. Though prior research focused on studying user resistance at the early stages of implementation, research on user resistance at the post-acceptance stage could generate knowledge applicable to situations where the systems in use are already accepted. The aim of this study was to gain an understanding of the challenges organizations within the processing industry may encounter when dealing with user resistance, and to suggest how these challenges could be managed. This was achieved by identifying factors that drive user resistance in a post-acceptance stage among machine operators at a paper production factory. Through the lens of a theoretical framework we identified six organization-level, individual-level and technological-level factors: communication deficiencies, lack of training, external system support, distance to management, lack of motivation and negative attitude towards systems. Based on those, five guiding principles related to managing continued IS-use were identified: update existing systems incrementally, engage employees through participation in organizational change, consolidate communication channels to external system suppliers, responsibility of system training and management of correct system usage. Finally, the study suggests implications for future research on user resistance.

Business Intelligence and Analytics have changed the business needs, but the market requires a more data-driven decision-making environment. Self-Service Business Intelligence initiatives are currently providing more competitive advantages. The role of the users and freedom of access is one of the essential advantages that SSBI holds. Despite this fact, there is still needed analysis on how business can gain more value from SSBI, based on the technological, operational and organizational aspects. The work in this thesis serves to analysis on the SSBI requirements that bring value to business. The paper is organized starting from building knowledge on the existing literature and exploring the domain. Data will be collected by interviewing experts within the BI, SSBI and IT fields. The main findings of the study show that on the technological aspect, data is more governed and its quality is improved by implementing SSBI. Visualization is one of the features of SSBI that boosts quality and governance. On the digital capability aspect, the end-users need training and there is found a rate of impact of SSBI on the main departments in an organization. It is discussed how SSBI implementation affects the companies that do not have BI solution. The final conclusions show that in order for SSBI to be successful, a solid BI environment is necessary. This research will provide future suggestions related to the topic and the results will serve both, the companies that have implemented SSBI and the ones that want to see it as a perspective in the future.

Graphite is the strongest diamagnet of all known materials to date. Recent studies of the thickness dependence of the resistance of graphite have demonstrated the heterogeneity of the charge distribution in bulk graphite and prompted the study of its magnetic properties. The studies of the thickness dependence of the magnetic susceptibility of graphite, done in this work, showed that two-dimensional interfaces between the crystalline (Bernal or rhombohedral stacking order) blocks in graphite make a dominant contribution to its diamagnetic susceptibility. Previously proposed models of diamagnetism in graphite are not suitable for explaining its magnetic properties, and therefore new concepts should be considered. Additionally, the studies of the transport and magnetic properties of graphite and multilayer graphene indicated the existence of superconductivity at the interfaces in well-ordered graphite. The possibility of creating permanent circulating currents around artificial holes in highly oriented graphite was studied by highly sensitive magnetization measurements. The obtained results provide hints for the possible existence of superconducting regions inside the bulk highly ordered graphite. In the present thesis, a further thickness dependent phenomenon on the depinning line (DL) of the flux line lattice of the high-Tc superconductor Bi2Sr2CaCu2O8+δ was studied. This geometrical effect shifts to notably lower temperatures in micrometer ring, compared with bulk crystals and thin flakes. The shift is related to a decrease in the overall pinning potential as a result of size effects, caused by: a) the thickness of the sample being smaller than the pinning correlation length, and b) the increase in the effective London penetration depth of the vortices (Pearl vortices). The large shift of the DL to lower temperatures may significantly influence the suitability of such elements for device applications in microstrip antennas and THz emitters.

abstract: This dissertation presents a multifaceted examination of the complex sociopolitical contexts of contemporary popular and classical music in Russia. By attending to the competing expectations of Russian creators, government officials, impresarios, critics, and listeners, it examines how contemporary musical artists have navigated the shifting nationalistic and popular moods of the past two decades. I argue that popular music artists Olga Kormukhina and Polina Gagarina, composer Rodion Shchedrin, and the Mariinsky Theater have transformed the works of past artists, including Viktor Tsoi and Nikolai Leskov, updating them according to a popular demand for patriotic works that the Russian state has cultivated through its media outlets and official pronouncements on cultural policy. Other rock musicians (Konstantin Kinchev and the band Bi-2) have also transformed their political identities to match the present-day demands and expectations of either Russian officialdom or their particular Russian audiences. With the exception of Bi-2 (an ambiguous counterexample), all of these transformations have led to greater associations with nationalistic sentiments or fervent support for state agendas in the contemporary geopolitical arena. Exploring the wide variety of styles and genres in this dissertation required a methodological versatility involving archival research, reception history, the analysis of musical scores and sound recordings, an examination of prose and poetic texts, and close study of visual imagery in music videos and onstage. The approach to reception history is the most groundbreaking, for it considers a wide range of digital sources, including blogs and social media comment threads, and makes use of language partner apps to augment the pool of informants, allowing conversation with Russians living outside the limited geographical range (St. Petersburg and Moscow) considered by previous studies. This holistic approach to contemporary reception history helps us to better understand how Russian audiences from diverse regions perceive these ongoing transformations. / Dissertation/Thesis / Doctoral Dissertation Music 2020