This diploma thesis deals with perspectives of application of service AC Capture from company AutoCont in the Slovak republic. AC Capture consists of offering digitizing services via cloud. Slovak republic is chosen as an inspected market mainly due to vendors licence policy which says, that IBM Datacap taskmaster Capture (platform for AC Capture) can be sold by company AutoCont only in the Czech or Slovak republic. Aiming at one market, which is in accordance with the licence policy, enables to go into appropriate depth and practically demonstrate the conclusions of the thesis via actual open business cases. The aim of the thesis is to evaluate the perspectives of application of AC Capture in the Slovak republic. The emphasis is placed on the macroeconomic analysis, analysis of indicators of standards of life, product analysis including the analysis of competition and quantitative questionnaire inquiry.

Subject of this thesis is to design a web application for overhead calculation, whose purpose is to be a substitution for presently used solution, which is considered to be deprecated. This is being done as a part of strategy of SAP SE corporation for which the solution is designed. This ambition to develop and improve cloud portfolio of already existing applications of the company should lead to betterment of already existing applications of this type and in a long run an improvement of the company’s market position as well as it’s products. The thesis is divided into 3 parts. It begins with a description of theoretical concepts, tools and principles, which are then utilized in further chapters. The following chapter analyzes current state of the affairs, where it is illustrated, what the current solution looks like along with key parts of it. The final, 3rd chapter is dedicated to a description of implemented solution and it also closely describes key differences mentioned in chapter 2.

The content of this master’s thesis is the analysis of human resources governance and its current state, the projecting of business processes and asset management owing to which the incoming information system will be selected and implemented. Undoubtedly, the company separation from its erstwhile owner, which ensured the termination of the present system’s technical support, represented the main initiative for the realization of the new system.

A lack of quality data on high clouds has led to inadequate representations within global weather and climate models. Recent advances in spaceborne measurements of the Earth’s atmosphere have provided complementary information on the interior of these clouds. This study demonstrate how an array of space-borne measurements can be used and combined, by close co-located comparisons in space and time, to form a more complete representation of high cloud processes and properties. High clouds are found in the upper atmosphere, where sub-zero temperatures frequently result in the formation of cloud particles that are composed of ice. Weather and climate models characterise the bulk properties of these ice particles to describe the current state of the cloud-sky atmosphere. By directly comparing measurements with simulations undertaken at the same place and time, this study demonstrates how improvements can be made to the representation of cloud properties. The results from this study will assist in the design of future cloud missions to provide a better quality input. These improvements will also help improve weather predictions and lower the uncertainty in cloud feedback response to increasing atmospheric temperature. Most clouds are difficult to monitor by more than one instrument due to continuous changes in: large-scale and sub-cloud scale circulation features, microphysical properties and processes and characteristic chemical signatures. This study undertakes co-located comparisons of high cloud data with a cloud ice dataset reported from the Microwave Limb Sounder (MLS) instrument onboard the Aura satellite that forms part of the A-train constellation. Data from the MLS science team include vertical profiles of temperature, ice water content (IWC) and the mixing ratios of several trace gases. Their vertical resolutions are 3 to 6 km. Initial investigations explore the link between cloud-top properties and the longwave radiation budget, developing methods for estimating cloud top heights using; longwave radiative fluxes, and IWC profiles. Synergistic trios of direct and indirect high cloud measurements were used to validate detections from the MLS by direct comparisons with two different A-train instruments; the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) and the Clouds and the Earth’s Radiant Energy System (CERES) onboard on the Aqua satellite. This finding focuses later studies on two high cloud scene types that are well detected by the MLS; deep convective plumes that form from moist ascent, and their adjacent outflows that emanate outwards several hundred kilometres. The second part of the thesis identifies and characterises two different high cloud scenes in the tropics. Direct observational data is used to refine calculations of the climate sensitivity to upper tropospheric humidity and high cloud in different conditions. The data reveals several discernible features of convective outflows are identified using a large sample of MLS data. The key finding, facilitated by the use of co-location, reveals that deep convective plumes exert a large longwave warming effect on the local climate of 52 ± 28Wm−2, with their adjacent outflows presenting a more modest warming of 33 ± 20Wm−2.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-11-22T20:38:08Z No. of bitstreams: 1 ClauberGomesBezerra_TESE.pdf: 7864722 bytes, checksum: 17c21362443f4d25511a0a211d52b805 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-11-23T23:24:44Z (GMT) No. of bitstreams: 1 ClauberGomesBezerra_TESE.pdf: 7864722 bytes, checksum: 17c21362443f4d25511a0a211d52b805 (MD5) / Made available in DSpace on 2017-11-23T23:24:44Z (GMT). No. of bitstreams: 1 ClauberGomesBezerra_TESE.pdf: 7864722 bytes, checksum: 17c21362443f4d25511a0a211d52b805 (MD5) Previous issue date: 2017-05-24 / Nesta tese apresentamos uma nova abordagem para realizar o agrupamento e a classifica??o de um conjunto de dados de forma n?o supervisionada. A abordagem proposta utiliza os conceitos de tipicidade e excentricidade usados pelo algoritmo TEDA na detec??o de outliers. Para realizar o agrupamento e a classifica??o ? proposto um algoritmo estat?stico chamado Auto-Cloud. As amostras analisadas pelo Auto-Cloud s?o agrupadas em unidades chamadas de data clouds, que s?o estruturas que n?o possuem formato ou limites definidos. O Auto-Cloud permite que cada amostra analisada possa pertencer simultaneamente a v?rias data clouds. O Auto-Cloud ? um algoritmo aut?nomo e evolutivo, que n?o necessita de treinamento ou qualquer conhecimento pr?vios sobre o conjunto de dados analisado. Ele permite a cria??o e a fus?o das data clouds de forma aut?noma, ? medida que as amostras s?o lidas, sem qualquer interven??o humana. As caracter?sticas do algoritmo fazem com que ele seja indicado para o agrupamento e classifica??o de streams de dados e para aplica??es que requerem respostas em tempo-real. O Auto- Cloud tamb?m ? um algoritmo recursivo, o que o torna r?pido e exige pouca quantidade de mem?ria. J? no processo de classifica??o dos dados, o Auto-Cloud trabalha como um classificador fuzzy, calculando o grau de pertin?ncia entre a amostra analisada e cada data cloud criada no processo de agrupamento. A classe a que pertence cada amostra ? determinada pela data cloud com maior grau de pertin?ncia com rela??o a amostra. Para validar o m?todo proposto, aplicamos o mesmo em v?rios conjuntos de dados existentes na literatura sobre o assunto. Al?m disso, o m?todo tamb?m foi validado numa aplica??o de detec??o e classifica??o de falhas em processos industriais, onde foram utilizados dados reais, obtidos de uma planta industrial. / In this thesis we propose a new approach to unsupervised data clustering and classification. The proposed approach is based on typicality and eccentricity concepts. This concepts are used by recently introduced TEDA algorithm for outlier detection. To perform data clustering and classification, it is proposed a new statistical algorithm, called Auto-Cloud. The data samples analyzed by Auto-Cloud are grouped in the form of unities called data clouds, which are structures without pre-defined shape or boundaries. Auto-Cloud allows each data sample to belong to multiple data clouds simultaneously. Auto-Cloud is an autonomous and evolving algorithm, which does not requires previous training or any prior knowledge about the data set. Auto-Cloud is able to create and merge data clouds autonomously, as data samples are obtained, without any human interference. The algorithm is suitable for data clustering and classification of online data streams and application that require real-time response. Auto-Cloud is also recursive, which makes it fast and with little computational effort. The data classification process works like a fuzzy classifier using the degree of membership between the analyzed data sample to each data cloud created in clustering process. The class to which each data sample belongs is determined by the cloud with the highest activation with respect to that sample. To validate the proposed method, we apply it to several existing datasets for data clustering and classification. Moreover, the method was also used in a fault detection in industrial processes application. In this case, we use real data obtained from a real world industrial plant.

This diploma thesis deals with suitable choice of Human Resources control informational system serving a larger technological company. It also contains processing of chosen analysis to identify shortcomings in problematic fields of the company. Obtained pieces of knowledge are used to the solution proposal in context of the issues of choice and following implementation of informational systems into the company.

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.

The role of data analytics increases in several application domains to cope with the large amount of captured data. Generally, data analytics are data-intensive processes, whose efficient execution is a challenging task. Each process consists of a collection of related structured activities, where huge data sets have to be exchanged between several loosely coupled services. The implementation of such processes in a service-oriented environment offers some advantages, but the efficient realization of data flows is difficult. Therefore, we use this paper to propose a novel SOA-aware approach with a special focus on the data flow. The tight interaction of new cloud technologies with SOA technologies enables us to optimize the execution of data-intensive service applications by reducing the data exchange tasks to a minimum. Fundamentally, our core concept to optimize the data flows is found in data clouds. Moreover, we can exploit our approach to derive efficient process execution strategies regarding different optimization objectives for the data flows.