No description available.

Universiẗat, Diss., 2001--Dortmund.

Data warehouses contain data consolidated from several operational databases and provide the historical, and summarized data which is more appropriate for analysis than detail, individual records. On-Line Analytical Processing (OLAP) provides advanced analysis tools to extract information from data stored in a data warehouse. Fast response time is essential for on-line decision support. A bitmap index could reach this goal in read-mostly environments. When data has high cardinality, we prefer to use the Range-Based Index (RBI), which divides the attributes values into several partitions and a bitmap vector is used to represent a range. With RBI, however, the number of records assigned to different ranges can be highly unbalanced, resulting in different search times of disk accesses for different queries. Wu et al proposed an algorithm for RBI, DBEC, which takes the data distribution into consideration. But the DBEC strategy could not guarantee to get the partition result with the given number of bitmap vectors, PN. Moreover, for different data records with the same value, they may be partitioned into different bitmap vectors which takes long disk I/O time. Therefore, we propose the IPDF, CP, CP* strategies for constructing the dynamic range-based indexes concerning with the case that data has high cardinality and is not uniformly distributed. The IPDF strategy decides each partition according to the Probability Density Function (p.d.f.). The CP strategy sorts the data and partitions them into PN groups for every w continuous records. The CP* strategy is an improved version of the CP strategy by adjusting the cutting points such that data records with the same value will be assigned into the same partition. On the other hand, we could take the history of users' queries into consideration. Based on the greedy approach, we propose the GreedyExt and GreedyRange strategies. The GreedyExt strategy is used for answering exact queries and the GreedyRange strategy is used for answering range queries. The two strategies decide the set of queries to construct the bitmap vectors such that the average response time of answering queries could be reduced. Moreover, a bitmap index consists of a set of bitmap vectors and the size of the bitmap index could be much larger than the capacity of the disk. We propose the FZ strategy to compress each bitmap vector to reduce the size of the storage space and provide efficient bitwise operations without decompressing these bitmap vectors. Finally, from our performance analysis, the performance of the CP* strategy could be better than the CP strategy in terms of the number of disk accesses. From our simulation, we show that the ranges divided by the IPDF and CP* strategies are more uniform than those divided by the DBEC strategy. The GreedyExt and GreedyRange strategies could provide fast response time in most of situations. Moreover, the FZ strategy could reduce the storage space more than the WAH strategy.

CPU schedulers are an integral part of all operating systems used in modern systems, as they enable efficient task execution. In recent years the demand for interactive tasks such as video playback, streaming and gaming has increased rapidly. This combined with new ideas and design for CPU scheduling has led to the development of the BitMap Queue scheduler(BMQ) by Alfred Chen. Which aims to be efficient and scalable for interactive tasks, in hopes of becoming an alternative scheduler for Linux. This paper evaluates the performance of BMQ compared to the current default scheduler, the Completely Fair Scheduler(CFS). The methodology in the paper focuses on two key aspects latency and interactivity. The results display BMQ outperforming CFS in latency while maintaining similar performance in interactivity. However during heavier loads in the interactivity test BMQ outperformed CFS significantly. Thus the results suggest that BMQ provides reduced latency while providing at least similar and in some cases better interactive performance when compared to CFS.

No description available.

No description available.

Made available in DSpace on 2016-06-02T19:05:38Z (GMT). No. of bitstreams: 1 2652.pdf: 3404746 bytes, checksum: b3a10a77ac70bae2b29efed871dc75e4 (MD5) Previous issue date: 2009-08-26 / Universidade Federal de Minas Gerais / Geographic Data Warehouses (GDW) became one of the main technologies used in decision-making processes and spatial analysis since they provide the integration of Data Warehouses, On-Line Analytical Processing and Geographic Information Systems. As a result, a GDW enables spatial analyses together with agile and flexible multidimensional analytical queries over huge volumes of data. On the other hand, there is a challenge in a GDW concerning the query performance, which consists of retrieving data related to ad-hoc spatial query windows and avoiding the high cost of star-joins. Clearly, mechanisms to provide efficient query processing, as index structures, are essential. In this master s thesis, a novel index for GDW is introduced, namely the SB-index, which is based on the Bitmap Join Index and the Minimum Bounding Rectangle. The SB-index inherits the Bitmap Index legacy techniques and introduces them in GDW, as well as it enables support for predefined spatial attribute hierarchies. The SB-index validation was performed through experimental performance tests. Comparisons among the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicated that the SB-index significantly improves the elapsed time in query processing from 76% up to 96% with regard to queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. In addition, the impact of the increase in data volume on the performance was analyzed. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Moreover, in this master s thesis there is an experimental investigation on how does the spatial data redundancy affect query response time and storage requirements in a GDW? . Redundant and non-redundant GDW schemas were compared, concluding that redundancy is related to high performance losses. Then, aiming at improving query performance, the SB-index performance was evaluated on the redundant GDW schema. The results pointed out that SB-index significantly improves the elapsed time in query processing from 25% up to 99%. Finally, a specific enhancement of the SB-index was developed in order to deal with spatial data redundancy. With this enhancement, the minimum performance gain observed became 80%. / O Data Warehouse Geográfico (DWG) tornou-se uma das principais tecnologias de suporte à decisão, pois promove a integração de data warehouses, On-Line Analytical Processing e Sistemas de Informações Geográficas. Por isso, um DWG viabiliza a análise espacial aliada à execução de consultas analíticas multidimensionais envolvendo enormes volumes de dados. Por outro lado, existe um desafio relativo ao desempenho no processamento de consultas, que envolvem janelas de consulta espaciais ad-hoc e várias junções entre tabelas. Claramente, mecanismos para aumentar o desempenho do processamento de consultas, como as estruturas de indexação, são essenciais. Nesta dissertação, propõe-se um novo índice para DWG chamado SB-index, baseado no Índice Bitmap de Junção e no Retângulo Envolvente Mínimo. O SB-index herda todo o legado de técnicas do Índice Bitmap e o introduz no DWG. Além disso, ele provê suporte a hierarquias de atributos espaciais predefinidas. Este índice foi validado por meio de testes experimentais de desempenho. Comparações entre o SB-index, a junção estrela auxiliada pela R-tree e a junção-estrela auxiliada por GiST indicaram que o SB-index diminui significativamente o tempo de resposta do processamento de consultas roll-up e drill-down relacionadas aos predicados espaciais intersecta , está contido e contém , promovendo ganhos de 76% a 96%. Mostrou-se ainda que a variação do volume de dados não prejudica o desempenho do SB-index. Esta dissertação também investiga a seguinte questão: como a redundância de dados espaciais afeta um DWG? . Foram comparados os esquemas de DWG redundante e não-redundante. Observou-se que a redundância de dados espaciais acarreta prejuízos ao tempo de resposta das consultas e aos requisitos de armazenamento do DWG. Então, visando melhorar o desempenho do processamento de consultas, introduziu-se o SB-index no esquema de DWG redundante. Os ganhos de desempenho obtidos a partir desta ação variaram de 25% a 99%. Por fim, foi proposta uma melhoria sobre o SB-index a fim de lidar especificamente com a questão da redundância de dados espaciais. A partir desta melhoria, o ganho mínimo de desempenho tornou-se 80%.

Um ambiente de data warehousing oferece suporte ao processo de tomada de decisão. Ele consolida dados de fontes de informação distribuições, autônomas e heterogêneas em um único componente, o data warehouse, e realiza o processamento eficiente de consultas analíticas, denominadas OLAP (on-line analytical processing). Um data warehouse convencional armazena apenas dados alfanuméricos. Por outro lado, um data warehouse de imagens armazena, além desses dados convencionais, características intrínsecas de imagens, permitindo a realização de consultas analíticas estendidas com predicados de similaridade entre imagens. Esses ambientes demandam, portanto, a criação de estratégias que possibilitem o processamento eficiente dessas consultas complexas e custosas. Apesar de haver na literatura trabalhos voltados a índices bitmap para ambientes de data warehousing e métodos de acesso métricos para melhorar o desempenho de consultas por similaridade entre imagens, no melhor do nosso conhecimento, não há uma técnica que investigue essas duas questões em um mesmo contexto. Esta dissertação visa preencher essa lacuna na literatura por meio das seguintes contribuições: (i) proposta do ImageDWindex, um mecanismo para a otimização de consultas analíticas estendidas com predicados de similaridade entre imagens; e (ii) definição de diferentes estratégias de processamento de consultas sobre data warehouses de imagens usando o ImageDW-index. Para validar as soluções propostas, foram desenvolvidas duas outras contribuições secundárias, que são: (iii) o ImageDW-Gen, um gerador de dados com o objetivo de povoar o data warehouse de imagens; e (iv) a proposta de quatro classes de consulta, as quais enfocam em diferentes custos de processamento dos predicados de similaridade entre imagens. Utilizando o ImageDW-Gen, foram realizados testes de desempenho para investigar as vantagens introduzidas pelas estratégias propostas, de acordo com as classes de consultas definidas. Comparado com o trabalho mais correlato existente na literatura, o uso do ImageDWindex proveu uma melhora no desempenho do processamento de consultas IOLAP que variou em média de 55,57% até 82,16%, considerando uma das estratégias propostas. / A data warehousing environment offers support to the decision-making process. It consolidates data from distributed, autonomous and heterogeneous information sources into one of its main components, the data warehouse. Furthermore, it provides effcient processing of analytical queries (i.e. OLAP queries). A conventional data warehouse stores only alphanumeric data. On the other hand, an image data warehouse stores not only alphanumeric data but also intrinsic features of images, thus allowing data warehousing environments to perform analytical similarity queries over images. This requires the development of strategies to provide efficient processing of these complex and costly queries. Although there are a number of approaches in the literature aimed at the development of bitmap index for data warehouses and metric access methods for the efficient processing of similarity queries over images, to the best of our knowledge, there is not an approach that investigate these two issues in the same setting. In this research, we fill this gap in the literature by introducing the following main contributions: (i) the proposal of the ImageDW-index, an optimization mechanism aimed at the efficient processing of analytical queries extended with similarity predicates over images; and (ii) definition of different processing strategies for image data warehouses using the ImageDW-index. In order to validate these main proposals, we also introduce two secondary contributions, as follows: (iii) the ImageDW-Gen, a data generator to populate image data warehouses; and (iv) the proposal of four query classes, each one enforcing different query processing costs associated to the similarity predicates in image data warehousing environments. Using the ImageDW-Gen, performance tests were carried out in order to investigate the advantages introduced by the proposed strategies, according to the query classes. Compared to the most related work available in the literature, the ImageDW-index provided a performance gain that varied from 55.57% to 82.16%, considering one of the proposed strategies.

Made available in DSpace on 2016-06-02T19:06:04Z (GMT). No. of bitstreams: 1 5138.pdf: 2676227 bytes, checksum: 72ab4695bfe8833d7d34d1e803a6ec9a (MD5) Previous issue date: 2012-12-13 / Financiadora de Estudos e Projetos / The growing concern with the support of the decision-making process has made companies to search technologies that support their decisions. The technology most widely used presently is the Data Warehouse (DW), which allows storing data so it is possible to produce useful and reliable information to assist in strategic decisions. Combining the concepts of Spatial Data Warehouse (SDW), that allows geometry storage and managing, and Temporal Data Warehouse (TDW), which allows storing data changes that occur in the real-world, a research topic known as Spatio-Temporal Data Warehouse (STDW) has emerged. STDW are suitable for the treatment of geometries that change over time. These technologies, combined with the steady growth volume of data, show the necessity of index structures to improve the performance of analytical query processing with spatial predicates and also with geometries that may vary over time. In this sense, this work focused on proposing an index for STDW called Spatio-Temporal Bitmap Index, or STB-index. The proposed index was designed to processing drill-down and roll-up queries considering the existence of predefined spatial hierarchies and with spatial attributes that can vary its position and shape over time. The validation of STB-index was performed by conducting experimental tests using a DWET created from synthetic data. Tests evaluated the elapsed time and the number of disk accesses to construct the index, the amount of storage space of the index and the elapsed time and the number of disk accesses for query processing. Results were compared with query processing using database management system resources and STBindex improved the query performance by 98.12% up to 99.22% in response time compared to materialized views. / A crescente preocupação com o suporte ao processo de tomada de decisão estratégica fez com que as empresas buscassem tecnologias que apoiassem as suas decisões. A tecnologia mais utilizada atualmente é a de Data Warehouse (DW), que permite armazenar dados de forma que seja possível produzir informação útil e confiável para auxiliar na tomada de decisão estratégica. Aliando-se os conceitos de Data Warehouse Espacial (DWE), que permite o armazenamento e o gerenciamento de geometrias, e de Data Warehouse Temporal (DWT), que possibilita representar as mudanças nos dados que ocorrem no mundo real, surgiu o tema de pesquisa conhecido por Data Warehouse Espaço-Temporal (DWET), que é próprio para o tratamento de geometrias que se alteram ao longo do tempo. Essas tecnologias, aliadas ao constante crescimento no volume de dados armazenados, evidenciam a necessidade de estruturas de indexação que melhorem o desempenho do processamento de consultas analíticas com predicados espaciais e com variação das geometrias no tempo. Nesse sentido, este trabalho se concentrou na proposta de um índice para DWET denominado Spatio- Temporal Bitmap Index, ou STB-index. O índice proposto foi projetado para o processamento de consultas do tipo drill-down e roll-up considerando a existência de hierarquias espaciais predefinidas, sendo que os atributos espaciais podem variar sua posição e sua forma ao longo do tempo. A validação do STB-index ocorreu por meio da realização de testes experimentais utilizando um DWET criado a partir de dados sintéticos. Os testes avaliaram o tempo e o número de acessos a disco para a construção do índice, a quantidade de espaço para armazenamento do índice e o tempo e número de acessos a disco para o processamento de consultas analíticas. Os resultados obtidos foram comparados com o processamento de consultas utilizando os recursos disponíveis dos sistemas gerenciadores de banco de dados, sendo que o STB-index apresentou um ganho de desempenho entre 98,12% e 99,22% no tempo de resposta das consultas se comparado ao uso de visões materializadas.

Submitted by Daniele Amaral (daniee_ni@hotmail.com) on 2016-10-07T20:05:05Z No. of bitstreams: 1 DissCJN.pdf: 5948964 bytes, checksum: e7e719e26b50a85697e7934bde411070 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T19:30:58Z (GMT) No. of bitstreams: 1 DissCJN.pdf: 5948964 bytes, checksum: e7e719e26b50a85697e7934bde411070 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T19:31:04Z (GMT) No. of bitstreams: 1 DissCJN.pdf: 5948964 bytes, checksum: e7e719e26b50a85697e7934bde411070 (MD5) / Made available in DSpace on 2016-10-20T19:31:09Z (GMT). No. of bitstreams: 1 DissCJN.pdf: 5948964 bytes, checksum: e7e719e26b50a85697e7934bde411070 (MD5) Previous issue date: 2016-03-08 / Não recebi financiamento / By providing ever-growing processing capabilities, many database technologies have been becoming important support tools to enterprises and institutions. The need to include (and control) new data types to the existing database technologies has brought also new challenges and research areas, arising the spatial, temporal, and spatiotemporal databases. Besides that, new analytical capabilities were required facilitating the birth of the data warehouse technology and, once more, the need to include spatial or temporal data (or both) to it, thus originating the spatial, temporal, and spatio-temporal data warehouses. The queries used in each database type had also evolved, culminating in the STOLAP (Spatio Temporal OLAP) queries, which are composed of predicates dealing with conventional, spatial, and temporal data with the possibility of having their execution aided by specialized index structures. This work’s intention is to investigate how the execution of each predicate affects the performance of STOLAP queries by varying the used indexes, their execution order and the query’s selectivity. Bitmap Join Indexes will help in conventional predicate’s execution and in some portions of the temporal processing, which will also count with the use of SQL queries for some of the alternatives used in this research. The SB-index and HSB-index will aid the spatial processing while the STB-index will be used to process temporal and spatial predicates together. The expected result is an analysis of the best predicate order while running the queries also considering their selectivity. Another contribution of this work is the evolution of the HSB-index to a hierarchized version called HSTB-index, which should complement the execution options. / Por proverem uma capacidade de processamento de dados cada vez maior, várias tecnologias de bancos de dados têm se tornado importantes ferramentas de apoio a empresas e instituições. A necessidade de se incluir e controlar novos tipos de dados aos bancos de dados já existentes fizeram também surgir novos desafios e novas linhas de pesquisa, como é o caso dos bancos de dados espaciais, temporais e espaçotemporais. Além disso, novas capacidades analíticas foram se fazendo necessárias culminando com o surgimento dos data warehouses e, mais uma vez, com a necessidade de se incluir dados espaciais e temporais (ou ambos) surgindo os data warehouses espaciais, temporais e espaço-temporais. As consultas relacionadas a cada tipo de banco de dados também evoluíram culminando com as consultas STOLAP (Spatio-Temporal OLAP) que são compostas basicamente por predicados envolvendo dados convencionais, espaciais e temporais e cujo processamento pode ser auxiliado por estruturas de indexação especializadas. Este trabalho pretende investigar como a execução de cada um dos tipos de predicados afeta o desempenho de consultas STOLAP variando-se os índices utilizados, a ordem de execução dos predicados e a seletividade das consultas. Índices Bitmap de Junção auxiliarão na execução dos predicados convencionais e de algumas partes dos predicados temporais que também contarão com o auxílio de consultas SQL, enquanto os índices SB-index e HSB-index serão utilizados para auxiliar na execução dos predicados espaciais das consultas. O STB-index também será utilizado nas comparações e envolve ambos os predicados espacial e temporal. Espera-se obter uma análise das melhores opções de combinação de execução dos predicados em consultas STOLAP tendo em vista também a seletividade das consultas. Outra contribuição deste trabalho é a evolução do HSB-index para uma versão hierarquizada chamada HSTB-index e que servirá para complementar as opções de processamento de consultas STOLAP.