Made available in DSpace on 2016-06-02T19:05:54Z (GMT). No. of bitstreams: 1 3993.pdf: 3294402 bytes, checksum: 982c043143364db53c8a4e2084205995 (MD5) Previous issue date: 2011-08-26 / A data warehousing environment supports the decision-making process through the investigation and analysis of data in an organized and agile way. However, the current data warehousing technologies do not allow that the decision-making processe be carried out based on images pictorial (intrinsic) features. This analysis can not be carried out in a conventional data warehousing because it requires the management of data related to the intrinsic features of the images to perform similarity comparisons. In this work, we propose a new data warehousing environment called iCube to enable the processing of OLAP perceptual similarity queries over images, based on their pictorial (intrinsic) features. Our approach deals with and extends the three main phases of the traditional data warehousing process to allow the use of images as data. For the data integration phase, or ETL phase, we propose a process to represent the image by its intrinsic content (such as color or texture numerical descriptors) and integrate this data with conventional data in the DW. For the dimensional modeling phase, we propose a star schema, called iStar, that stores both the intrinsic and the conventional image data. Moreover, at this stage, our approach models the schema to represent and support the use of different user-defined perceptual layers. For the data analysis phase, we propose an environment in which the OLAP engine uses the image similarity as a query predicate. This environment employs a filter mechanism to speed-up the query execution. The iStar was validated through performance tests for evaluating both the building cost and the cost to process IOLAP queries. The results showed that our approach provided an impressive performance improvement in IOLAP query processing. The performance gain of the iCube over the best related work (i.e. SingleOnion) was up to 98,21%. / Um ambiente de data warehousing (DWing) auxilia seus usuários a tomarem decisões a partir de investigações e análises dos dados de maneira organizada e ágil. Entretanto, os atuais recursos de DWing não possibilitam que o processo de tomada de decisão seja realizado com base em comparações do conteúdo intrínseco de imagens. Esta análise não pode ser realizada por aplicações de DW convencionais porque essa utiliza, como base, imagens digitais e necessita realizar operações baseadas em similaridade, para as quais um DW convencional não oferece suporte. Neste trabalho, é proposto um ambiente de data warehouse chamado iCube que provê suporte ao processamento de consultas IOLAP (Image On-Line Analytical Processing) baseadas em diversas percepções de similaridade entre as imagens. O iCube realiza adaptações nas três principais fases de um ambiente de data warehousing convencional para permitir o uso de imagens como dados de um data warehouse (DW). Para a fase de integração, ou fase ETL (Extract, Trasnform and Load), nós propomos um processo para representar as imagens a partir de seu conteúdo intrínseco (i.e., por exemplo por meio de descritores numéricos que representam cor ou textura dessas imagens) e integrar esse conteúdo intrínseco a dados convencionais em um DW. Neste trabalho, nós também propomos um esquema estrela otimizado para o iCube, denominado iStar, que armazena tanto dados convencionais quanto dados de representação do conteúdo intrínseco das imagens. Ademais, nesta fase, o iStar foi projetado para representar e prover suporte ao uso de diferentes camadas perceptuais definidas pelo usuário. Para a fase de análise de dados, o iCube permite que processos OLAP sejam executados com o uso de comparações de similaridade como predicado de consultas e com o uso de mecanismos de filtragem para acelerar o processamento de consultas OLAP. O iCube foi validado a partir de testes de desempenho para a construção da estrutura e para o processamento de consultas IOLAP. Os resultados demonstraram que o iCube melhora significativamente o desempenho no processamento de consultas IOLAP quando comparado aos atuais recursos de IDWing. Os ganhos de desempenho do iCube contra o melhor trabalho correlato (i.e. SingleOnion) foram de até 98,21%.

The main goal of this thesis is to summarize and introduce general theoretical concepts of Data Warehousing by using the systems approach. The thesis defines Data Warehousing and its main areas and delimitates Data Warehousing area in terms of higher-level area called Business Intelligence. It also describes the history of Data Warehousing & Business Intelligence, focuses on key principals of Data Warehouse building and explains the practical applications of this solution. The aim of the practical part is to perform the evaluation of theoretical concepts. Based on that, design and build Data Warehouse in environment of an existing company. The final solution shall include Data Warehouse design, hardware and software platform selection, loading with real data by using ETL services and building of end users reports. The objective of the practical part is also to demonstrate the power of this technology and shall contribute to business decision-making process in this company.

Theme of this master’s thesis is design of software support for business intelligence. Design is realized in cooperation with corporation ZZN Pelhřimov a.s. Introduction is focused on theoretical description of business intelligence and datamining and also on development environment in which is project designed. Corporation is characterised also in introduction. Main part contains data collecting and definition of individual modules. In conclusion of this thesis will be several types of analysis from collected data and then according to these analysis, we can draw measures to improve current state of corporation.

This Master's thesis deals with the principles of the data mining process, especially with the mining  of association rules. The theoretical apparatus of general description and principles of the data warehouse creation is set. On the basis of this theoretical knowledge, the application for the association rules mining is implemented. The application requires the data in the transactional form or the multidimensional data organized in the Star schema. The implemented algorithms for finding  of the frequent patterns are Apriori and FP-tree. The system allows the variant setting of parameters for mining process. Also, the validation tests and efficiency proofs were accomplished. From the point of view of the association rules searching support, the resultant application is more applicable and robust than the existing compared systems SAS Miner and Oracle Data Miner.

This paper deals with data warehouses and OLAP. These technologies are defined and described here. Then an introduction of the architecture of product MS SQL Server and its tools for work with data warehouses and OLAP folow. The knowledge gained is used for creation of sample application.