by Yeung Chung Kuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 104-105). / Chapter 1 --- Infinitesimal Deformation of Compact Complex Manifolds --- p.3 / Chapter 1.1 --- Differentiable Family --- p.3 / Chapter 1.2 --- Infinitesimal Deformation in Differentiable Family --- p.6 / Chapter 1.3 --- Trivial Differentiable Family --- p.8 / Chapter 1.4 --- Complex Analytic Family --- p.13 / Chapter 1.5 --- Induced Family --- p.19 / Chapter 2 --- Theorem of Existence --- p.22 / Chapter 2.1 --- Introduction --- p.22 / Chapter 2.2 --- "Some Facts on the qth Cohomology Group Hq(M,´ة）" --- p.23 / Chapter 2.3 --- Obstructions to Deformation --- p.24 / Chapter 2.4 --- An Elementary Method for Theorem of Existence --- p.26 / Chapter 2.5 --- Proof of Theorem of Existence --- p.35 / Chapter 3 --- "Comparison between the Number of Moduli m(M) and dim H1 (M,´ة）" --- p.64 / Chapter 3.1 --- Number of Moduli of Compact Complex Manifold --- p.64 / Chapter 3.2 --- Examples --- p.68 / Chapter 4 --- Theorem of Completeness --- p.84 / Chapter 4.1 --- Theorem of Completeness --- p.84 / Chapter 4.2 --- Construction of Formal Power Series of h and g --- p.86 / Chapter 4.3 --- Proof of Convergence --- p.93

Typescript (photocopy). / Digitized by Kansas Correctional Industries

L'intégration de l'information fournie par de multiples sources de données hétérogènes est un besoin croissant des systèmes d'information actuels. Dans ce contexte, les besoins des applications sont décrits au moyen d'un schéma cible et la façon dont les instances du schéma cible sont dérivées à partir des sources de données est exprimée par des mappings. Dans cette thèse, nous nous intéressons à la génération automatique de mappings pour des sources de données XML ainsi qu'à l'adaptation de ces mappings en cas de changements survenant dans le schéma cible ou dans les sources de données. <br />Nous proposons une approche de génération de mappings en trois phases : (i) la décomposition du schéma cible en sous-arbres, (ii) la recherche de mappings partiels pour chacun de ces sous-arbres et enfin (iii) la génération de mappings pour l'ensemble du schéma cible à partir de ces mappings partiels. Le résultat de notre approche est un ensemble de mappings, chacun ayant une sémantique propre. Dans le cas où l'information requise par le schéma cible n'est pas présente dans les sources, aucun mapping ne sera produit. Dans ce cas, nous proposons de relaxer certaines contraintes définies sur le schéma cible pour permettre de générer des mappings. Nous avons développé un outil pour supporter notre approche. Nous avons également proposé une approche d'adaptation des mappings existants en cas de changement survenant dans les sources ou dans le schéma cible.

Data warehouses were envisioned to facilitate analytical reporting and data visualization by providing a model for the flow of data from operational databases to decision support environments. Decision support environments provide a multidimensional conceptual view of the underlying data warehouse, which is usually stored in relational DBMSs. Typically, there is an impedance mismatch between this conceptual view — shared also by all decision support applications accessing the data warehouse — and the physical model of the data stored in relational DBMSs. This thesis presents a mapping compilation algorithm in the context of the Conceptual Integration Model (CIM) [67] framework. In the CIM framework, the relationships between the conceptual model and the physical model are specified by a set of attribute-to-attribute correspondences. The algorithm compiles these correspondences into a set of mappings that associate each construct in the conceptual model with a query on the physical model. Moreover, the homogeneity and summarizability of data in conceptual models is the key to accurate query answering, a necessity in decision making environments. A data-driven approach to refactor relational models into summarizable schemas and instances is proposed as the solution of this issue. We outline the algorithms and challenges in bridging multidimensional conceptual models and the physical model of data warehouses and discuss experimental results.

Data warehouses were envisioned to facilitate analytical reporting and data visualization by providing a model for the flow of data from operational databases to decision support environments. Decision support environments provide a multidimensional conceptual view of the underlying data warehouse, which is usually stored in relational DBMSs. Typically, there is an impedance mismatch between this conceptual view — shared also by all decision support applications accessing the data warehouse — and the physical model of the data stored in relational DBMSs. This thesis presents a mapping compilation algorithm in the context of the Conceptual Integration Model (CIM) [67] framework. In the CIM framework, the relationships between the conceptual model and the physical model are specified by a set of attribute-to-attribute correspondences. The algorithm compiles these correspondences into a set of mappings that associate each construct in the conceptual model with a query on the physical model. Moreover, the homogeneity and summarizability of data in conceptual models is the key to accurate query answering, a necessity in decision making environments. A data-driven approach to refactor relational models into summarizable schemas and instances is proposed as the solution of this issue. We outline the algorithms and challenges in bridging multidimensional conceptual models and the physical model of data warehouses and discuss experimental results.

Thesis (Ph.D.)--Ohio University, August, 2009. / Release of full electronic text on OhioLINK has been delayed until June 1, 2012. Includes bibliographical references (leaves 63-66)

Thesis (Ph.D.)--Ohio University, August, 2009. / Title from PDF t.p. Release of full electronic text on OhioLINK has been delayed until June 1, 2012. Includes bibliographical references (leaves 63-66)

Thesis (Ph. D.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 50-53).

No description available.

Data warehouses were envisioned to facilitate analytical reporting and data visualization by providing a model for the flow of data from operational databases to decision support environments. Decision support environments provide a multidimensional conceptual view of the underlying data warehouse, which is usually stored in relational DBMSs. Typically, there is an impedance mismatch between this conceptual view — shared also by all decision support applications accessing the data warehouse — and the physical model of the data stored in relational DBMSs. This thesis presents a mapping compilation algorithm in the context of the Conceptual Integration Model (CIM) [67] framework. In the CIM framework, the relationships between the conceptual model and the physical model are specified by a set of attribute-to-attribute correspondences. The algorithm compiles these correspondences into a set of mappings that associate each construct in the conceptual model with a query on the physical model. Moreover, the homogeneity and summarizability of data in conceptual models is the key to accurate query answering, a necessity in decision making environments. A data-driven approach to refactor relational models into summarizable schemas and instances is proposed as the solution of this issue. We outline the algorithms and challenges in bridging multidimensional conceptual models and the physical model of data warehouses and discuss experimental results.