This thesis deals with association rules mining over data warehouses. In the first part the reader will be familiarized with terms like knowledge discovery in databases and data mining. The following part of the work deals with data warehouses. Further the association analysis, the association rules, their types and mining possibilities are described. The architecture of Microsoft SQL Server and its tools for working with data warehouses are presented. The rest of the thesis includes description and analysis of the Star-miner algorithm, design, implementation and testing of the application.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2014-11-25T13:07:50Z No. of bitstreams: 2 Dissertação - Jorge Peixoto de Morais Neto - 2014.pdf: 1582808 bytes, checksum: 3115f942e2c8a9cf83601835af3af1c5 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-11-25T14:42:09Z (GMT) No. of bitstreams: 2 Dissertação - Jorge Peixoto de Morais Neto - 2014.pdf: 1582808 bytes, checksum: 3115f942e2c8a9cf83601835af3af1c5 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-11-25T14:42:09Z (GMT). No. of bitstreams: 2 Dissertação - Jorge Peixoto de Morais Neto - 2014.pdf: 1582808 bytes, checksum: 3115f942e2c8a9cf83601835af3af1c5 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-01-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Let M be a metric space and let P be a subset of M. The well known k-nearest neighbors problem (KNN) consists in finding, given q 2 M, the k elements of P with are closest to q according to the metric of M. We discuss a variation of KNN for a particular class of pseudo-metric spaces, described as follows. Let m 2 N be a natural number and let d be the Euclidean distance in Rm. Given p 2 Rm: p := (p1; : : : ; pm) let C (p) be the set of the m rotations of p’s coordinates: C (p) := f(p1; : : : ; pm); (p2; : : : ; pm; p1); : : : ; (pm; p1; : : : ; pm􀀀1)g we define the special distance de as: de(p;q) := min p02C (p) d(p0;q): de is a pseudo-metric, and (Rm;de) is a pseudo-metric space. The class of pseudo-metric spaces under discussion is f(Rm;de) j m 2 N:g The brute force approach is too costly for instances of practical size. We present a more efficient solution employing parallelism, the FFT (fast Fourier transform) and the fast elimination of unfavorable training vectors.We describe a program—named CyclicKNN —which implements this solution.We report the speedup of this program over serial brute force search, processing reference datasets. / Seja M um espaço métrico e P um subconjunto de M. O conhecido problema k vizinhos mais próximos (k-neareast neighbors, KNN) consiste em encontrar, dado q 2 M, os k elementos de P mais próximos de q conforme a métrica de M. Abordamos uma variação do problema KNN para uma classe particular de espaços pseudo-métricos, descrita a seguir. Seja m 2 N um natural e seja d a distância euclidiana em Rm. Dado um vetor p 2 Rm: p := (p1; : : : ; pm) seja C (p) o conjunto das m rotações das coordenadas de p: C (p) := f(p1; : : : ; pm); (p2; : : : ; pm; p1); : : : ; (pm; p1; : : : ; pm􀀀1)g definimos a distância especial de como: de(p;q) := min p02C (p) d(p0;q): de é uma pseudo-métrica, e (Rm;de) é um espaço pseudo-métrico. A classe de espaços pseudo-métricos abordada é (Rm;de) j m 2 N: A solução por força bruta é cara demais para instâncias de tamanho prático. Nós apresentamos uma solução mais eficiente empregando paralelismo, a FFT (transformada rápida de Fourier) e a eliminação rápida de vetores de treinamento desfavoráveis. Desenvolvemos um programa—chamado CyclicKNN—que implementa essa solução. Reportamos o speedup desse programa em comparação com a força bruta sequencial, processando bases de dados de referência.

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 thesis summarizes the information retrieval theory, the relational model basic and focuses on the data indexing in relational database systems. The thesis focuses on multimedia data searching. It includes description of automatic multimedia data content extraction and multimedia data indexing. Practical part discusses design and solution implementation for improving query effectivity for multidimensional vector similarity which describes multimedia data. Thesis final part discusses experiments with this solution.