Organizations own data sources that contain millions, billions or even trillions of rows and these data are usually highly dimensional in nature. Typically, these raw repositories are comprised of numerous independent data sources that are too big to be copied or joined, with the consequence that aggregations become highly problematic. Data cubes play an essential role in facilitating fast Online Analytical Processing (OLAP) in many multi-dimensional data warehouses. Current data cube computation techniques have had some success in addressing the above-mentioned aggregation problem. However, the combined problem of reducing data cube size for very large and highly dimensional databases, while guaranteeing fast query response times, has received less attention. Another issue is that most OLAP tools often causes users to be lost in the ocean of data while performing data analysis. Often, most users are interested in only a subset of the data. For example, consider in such a scenario, a business manager who wants to answer the crucial location-related business question. "Why are my sales declining at location X"? This manager wants fast, unambiguous location-aware answers to his queries. He requires access to only the relevant ltered information, as found from the attributes that are directly correlated with his current needs. Therefore, it is important to determine and to extract, only that small data subset that is highly relevant from a particular user's location and perspective. In this thesis, we present the Personalized Smart Cube approach to address the abovementioned scenario. Our approach consists of two main parts. Firstly, we combine vertical partitioning, partial materialization and dynamic computation to drastically reduce the size of the computed data cube while guaranteeing fast query response times. Secondly, our personalization algorithm dynamically monitors user query pattern and creates a personalized data cube for each user. This ensures that users utilize only that small subset of data that is most relevant to them. Our experimental evaluation of our Personalized Smart Cube approach showed that our work compared favorably with other state-of-the-art methods. We evaluated our work focusing on three main criteria, namely the storage space used, query response time and the cost savings ratio of using a personalized cube. The results showed that our algorithm materializes a relatively smaller number of views than other techniques and it also compared favourable in terms of query response time. Further, our personalization algorithm is superior to the state-of-the art Virtual Cube algorithm, when evaluated in terms of the number of user queries that were successfully answered when using a personalized cube, instead of the base cube.

Organizations own data sources that contain millions, billions or even trillions of rows and these data are usually highly dimensional in nature. Typically, these raw repositories are comprised of numerous independent data sources that are too big to be copied or joined, with the consequence that aggregations become highly problematic. Data cubes play an essential role in facilitating fast Online Analytical Processing (OLAP) in many multi-dimensional data warehouses. Current data cube computation techniques have had some success in addressing the above-mentioned aggregation problem. However, the combined problem of reducing data cube size for very large and highly dimensional databases, while guaranteeing fast query response times, has received less attention. Another issue is that most OLAP tools often causes users to be lost in the ocean of data while performing data analysis. Often, most users are interested in only a subset of the data. For example, consider in such a scenario, a business manager who wants to answer the crucial location-related business question. "Why are my sales declining at location X"? This manager wants fast, unambiguous location-aware answers to his queries. He requires access to only the relevant ltered information, as found from the attributes that are directly correlated with his current needs. Therefore, it is important to determine and to extract, only that small data subset that is highly relevant from a particular user's location and perspective. In this thesis, we present the Personalized Smart Cube approach to address the abovementioned scenario. Our approach consists of two main parts. Firstly, we combine vertical partitioning, partial materialization and dynamic computation to drastically reduce the size of the computed data cube while guaranteeing fast query response times. Secondly, our personalization algorithm dynamically monitors user query pattern and creates a personalized data cube for each user. This ensures that users utilize only that small subset of data that is most relevant to them. Our experimental evaluation of our Personalized Smart Cube approach showed that our work compared favorably with other state-of-the-art methods. We evaluated our work focusing on three main criteria, namely the storage space used, query response time and the cost savings ratio of using a personalized cube. The results showed that our algorithm materializes a relatively smaller number of views than other techniques and it also compared favourable in terms of query response time. Further, our personalization algorithm is superior to the state-of-the art Virtual Cube algorithm, when evaluated in terms of the number of user queries that were successfully answered when using a personalized cube, instead of the base cube.

Undergraduate honors paper--Mount Holyoke College, 2008. Dept. of Art and Art History. / Includes bibliographical references (leaf [13]).

Thesis (Ph. D.)--University of Wisconsin, 1967. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliography.

For finishing this thesis, I want to rethink the meaning of architecture after studying architecture in these years. I reviewed buildings in the world. In the process of reviewing, I analyzed these buildings by the method which called finite element method. The finite element method is usually used for analyzing the complex system of structure. In this system, The line is the basic element for a one dimension. The plane is the basic element for a two dimension. By following this logic, the cube is the basic element for the three dimension. After some researches for cube in architecture and geometry, I found there were a lot of examples for using cube to design. And for cube itself, there were many properties. Then, I decided to use cube to design the building in my thesis. And in the process for designing, I understood the meaning of architecture and design better than before. For finishing this design, I related Washington, D.C. with my design. So I designed a multi-function museum for this city to help people to understand how this city was built in the history. As a landmark building, this building, "Impressions of DC," is a result of this thesis study and exploration of the cube. / Master of Architecture / It is easy to build a building. But for building a city, it is much harder. For building a city, this is a long process. And in this process, many important moments would happen. The city, Washington, D.C., was also built after many important moments. As one of most important capitals in the world, this city has suffered many disasters and survived in the process of history. There are many museums in this city to record the history of this country, but there is not a good museum to record the history of this city. So, I decided to design a building for this great city. This building would also become a good place to help people understand this city. This building would become a new landmark for this city. I decided to design this building in a special site, the Hains Point. This site is close to the Potomac River and Ronald Reagan Washington National Airport. So, it would be easy for that people visit this building. I also want this building become the first impression of DC for people. This is also my thesis title. I used my architecture knowledge and many people's help to design this cube building, "Impressions of DC".

This is a house situated by a river. It is a retreat, or a cabin, and is not occupied on a full time basis. Its form is an interpretation of a cube, and retains its general appearance. It is lifted off the ground to prevent damage when the river floods, and has a deep foundation to achieve stability on the loose floodplain soil. It has three levels, the first containing the main living areas, the second, a lofted space containing the bed and bath, and the third, a roof terrace. It is for two people, and so has a small footprint of about thirty two feet square. The footprint is subdivided into nine squares, with a fireplace and chimney occupying the center and habitable spaces surrounding the periphery. Its foundation is of a concrete two way flat slab supported by concrete caissons, its walls are of reinforced concrete masonry units, and its floors and ceilings are of small timber frame construction. / Master of Architecture

Reproduction de : Thèse de 3e cycle : mathématiques appliquées : Grenoble 1 : 1981. Reproduction de : Thèse de 3e cycle : mathématiques appliquées : Grenoble, INPG : 1981. / Titre provenant de l'écran-titre. Bibliogr. p. 167-170.

No description available.

Monodisperse Pd nanocubes of 20 nm rib length and Pd nanospheres of 3 nm diameter deposited on corundum were used as efficient tool to reveal structure sensitivity of three-phase hydrogenations of unsaturated alcohols. For an olefin alcohol hydrogenation in the kinetic regime, surface (100) atoms of the cubes displayed lower activity than other surface atoms of the spheres. Apparent activation energies of 23 kJ/mol for the cubes and 17 kJ/mol for the spheres confirmed the reaction structure sensitivity. In an acetylenic alcohol hydrogenation, the cubes showed higher selectivity to an olefinic product than the spheres. Apparent activation energy was found as 38 kJ/mol for the cubes and 24 kJ/mol for the spheres. The apparent structure sensitivity in this case was attributed to liquid-solid mass transfer limitations governing the sphere-catalyzed reactions. The study shows the applicability and limitations of the use of nanoparticles for structure sensitivity studies in catalysis. / Chemical Engineering

Thesis (Ph. D.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains vi, 72 p. Includes abstract. Includes bibliographical references (p. 69-72).