Schema merging is the process of incorporating data models into an integrated, consistent schema from which query solutions satisfying all incorporated models can be derived. The efficiency of such a process is reliant on the effective semantic representation of the chosen data models, as well as the mapping relationships between the elements of the source data models. Consider a scenario where, as a result of company mergers or acquisitions, a number of related, but possible disparate data marts need to be integrated into a global data warehouse. The ability to retrieve data across these disparate, but related, data marts poses an important challenge. Intuitively, forming an all-inclusive data warehouse includes the tedious tasks of identifying related fact and dimension table attributes, as well as the design of a schema merge algorithm for the integration. Additionally, the evaluation of the combined set of correct answers to queries, likely to be independently posed to such data marts, becomes difficult to achieve. Model management refers to a high-level, abstract programming language designed to efficiently manipulate schemas and mappings. Particularly, model management operations such as match, compose mappings, apply functions and merge, offer a way to handle the above-mentioned data integration problem within the domain of data warehousing. In this research, we introduce a methodology for the integration of star schema source data marts into a single consolidated data warehouse based on model management. In our methodology, we discuss the development of three (3) main streamlined steps to facilitate the generation of a global data warehouse. That is, we adopt techniques for deriving attribute correspondences, and for schema mapping discovery. Finally, we formulate and design a merge algorithm, based on multidimensional star schemas; which is primarily the core contribution of this research. Our approach focuses on delivering a polynomial time solution needed for the expected volume of data and its associated large-scale query processing. The experimental evaluation shows that an integrated schema, alongside instance data, can be derived based on the type of mappings adopted in the mapping discovery step. The adoption of Global-And-Local-As-View (GLAV) mapping models delivered a maximally-contained or exact representation of all fact and dimensional instance data tuples needed in query processing on the integrated data warehouse. Additionally, different forms of conflicts, such as semantic conflicts for related or unrelated dimension entities, and descriptive conflicts for differing attribute data types, were encountered and resolved in the developed solution. Finally, this research has highlighted some critical and inherent issues regarding functional dependencies in mapping models, integrity constraints at the source data marts, and multi-valued dimension attributes. These issues were encountered during the integration of the source data marts, as it has been the case of evaluating the queries processed on the merged data warehouse as against that on the independent data marts.

Schema merging is the process of incorporating data models into an integrated, consistent schema from which query solutions satisfying all incorporated models can be derived. The efficiency of such a process is reliant on the effective semantic representation of the chosen data models, as well as the mapping relationships between the elements of the source data models. Consider a scenario where, as a result of company mergers or acquisitions, a number of related, but possible disparate data marts need to be integrated into a global data warehouse. The ability to retrieve data across these disparate, but related, data marts poses an important challenge. Intuitively, forming an all-inclusive data warehouse includes the tedious tasks of identifying related fact and dimension table attributes, as well as the design of a schema merge algorithm for the integration. Additionally, the evaluation of the combined set of correct answers to queries, likely to be independently posed to such data marts, becomes difficult to achieve. Model management refers to a high-level, abstract programming language designed to efficiently manipulate schemas and mappings. Particularly, model management operations such as match, compose mappings, apply functions and merge, offer a way to handle the above-mentioned data integration problem within the domain of data warehousing. In this research, we introduce a methodology for the integration of star schema source data marts into a single consolidated data warehouse based on model management. In our methodology, we discuss the development of three (3) main streamlined steps to facilitate the generation of a global data warehouse. That is, we adopt techniques for deriving attribute correspondences, and for schema mapping discovery. Finally, we formulate and design a merge algorithm, based on multidimensional star schemas; which is primarily the core contribution of this research. Our approach focuses on delivering a polynomial time solution needed for the expected volume of data and its associated large-scale query processing. The experimental evaluation shows that an integrated schema, alongside instance data, can be derived based on the type of mappings adopted in the mapping discovery step. The adoption of Global-And-Local-As-View (GLAV) mapping models delivered a maximally-contained or exact representation of all fact and dimensional instance data tuples needed in query processing on the integrated data warehouse. Additionally, different forms of conflicts, such as semantic conflicts for related or unrelated dimension entities, and descriptive conflicts for differing attribute data types, were encountered and resolved in the developed solution. Finally, this research has highlighted some critical and inherent issues regarding functional dependencies in mapping models, integrity constraints at the source data marts, and multi-valued dimension attributes. These issues were encountered during the integration of the source data marts, as it has been the case of evaluating the queries processed on the merged data warehouse as against that on the independent data marts.

<p>Many maintenance policies have been proposed for refreshing a warehouse. The difficulties of selecting an appropriate maintenance policy for a specific scenario with specific source characteristics, user requirements etc. has triggered researcher to develop algorithms and cost-models for predicting cost associated with a policy and a scenario. In this dissertation, we develop a benchmarking tool for testing scenarios and retrieve real world data that can be compared against algorithms and cost-models. The approach was to support a broad set of configurations, including the support of source characteristics proposed in [ENG00], to be able to test a diversity set of scenarios.</p>

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Thomas J. Housel, Glenn R. Cook. Includes bibliographical references (p. 81-82). Also available online.

Schema merging is the process of incorporating data models into an integrated, consistent schema from which query solutions satisfying all incorporated models can be derived. The efficiency of such a process is reliant on the effective semantic representation of the chosen data models, as well as the mapping relationships between the elements of the source data models. Consider a scenario where, as a result of company mergers or acquisitions, a number of related, but possible disparate data marts need to be integrated into a global data warehouse. The ability to retrieve data across these disparate, but related, data marts poses an important challenge. Intuitively, forming an all-inclusive data warehouse includes the tedious tasks of identifying related fact and dimension table attributes, as well as the design of a schema merge algorithm for the integration. Additionally, the evaluation of the combined set of correct answers to queries, likely to be independently posed to such data marts, becomes difficult to achieve. Model management refers to a high-level, abstract programming language designed to efficiently manipulate schemas and mappings. Particularly, model management operations such as match, compose mappings, apply functions and merge, offer a way to handle the above-mentioned data integration problem within the domain of data warehousing. In this research, we introduce a methodology for the integration of star schema source data marts into a single consolidated data warehouse based on model management. In our methodology, we discuss the development of three (3) main streamlined steps to facilitate the generation of a global data warehouse. That is, we adopt techniques for deriving attribute correspondences, and for schema mapping discovery. Finally, we formulate and design a merge algorithm, based on multidimensional star schemas; which is primarily the core contribution of this research. Our approach focuses on delivering a polynomial time solution needed for the expected volume of data and its associated large-scale query processing. The experimental evaluation shows that an integrated schema, alongside instance data, can be derived based on the type of mappings adopted in the mapping discovery step. The adoption of Global-And-Local-As-View (GLAV) mapping models delivered a maximally-contained or exact representation of all fact and dimensional instance data tuples needed in query processing on the integrated data warehouse. Additionally, different forms of conflicts, such as semantic conflicts for related or unrelated dimension entities, and descriptive conflicts for differing attribute data types, were encountered and resolved in the developed solution. Finally, this research has highlighted some critical and inherent issues regarding functional dependencies in mapping models, integrity constraints at the source data marts, and multi-valued dimension attributes. These issues were encountered during the integration of the source data marts, as it has been the case of evaluating the queries processed on the merged data warehouse as against that on the independent data marts.

Thesis (M.C.S.) - Carleton University, 2005. / Includes bibliographical references (p. 88-93). Also available in electronic format on the Internet.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2010. / Title from PDF title page (viewed on Jul. 14, 2010). Includes bibliographical references.

Thesis (Ph. D.)--University of Rhode Island, 2004. / Typescript. Includes bibliographical references (leaves 81-87).

Made available in DSpace on 2014-06-11T19:32:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-09-23Bitstream added on 2014-06-13T20:04:45Z : No. of bitstreams: 1 porto_aj_dr_botfmvz.pdf: 265943 bytes, checksum: 8d780133f2f57ee636ce21bab63715dd (MD5) / Universidade Estadual Paulista (UNESP) / Conduzido na Unidade de Pesquisa e Desenvolvimento de Gália (APTA/SAA) em março de 2007, o trabalho teve por objetivo avaliar sistemas de armazenagem da amoreira, quanto à eficiência de conservação e tempo de armazenamento. Os ramos foram armazenados em quatro diferentes sistemas: em depósito de ramos e no barracão, cobertos com tecido úmido, com as extremidades basais imersas em água, e cobertos e com as extremidades imersas. O delineamento experimental utilizado foi em blocos ao acaso com parcelas subdivididas, cinco repetições (blocos), quatro tratamentos principais (parcelas) e cinco tratamentos secundários (subparcelas, um a cinco dias de armazenamento). O sistema onde os ramos de amoreira foram armazenados em local fechado, cobertos com tecido úmido e com as extremidades imersas em água foi mais eficiente, pois as folhas mantiveram teor de umidade apropriado para alimentação do bicho-da-seda (69,13%) por até quatro dias de armazenamento. / Carried out at Unidade de Pesquisa e Desenvolvimento de Gália (APTA/SAA) in march of 2007, the work had for objective to evaluate storage systens of mulberry, with regard to conservation efficiency and warehousing time. The branches were stored in four different systems: in branches depository and in the shelter, covered with wet cloth, with the basal extremities immersed in water, and covered and with the extremities immersed. The experimental design utilized was split plot, with five replications (blocks), four principal treatments (parcels) and five secondary treatments (sub parcels, one until five days of warehousing). The system where the mulberry branches were stored in close place, covered with wet cloth and with the extremities immersed in water was more efficient, because the leaves maintained appropriate moisture purport for silkworm feeding (69,13%) for until four days of warehousing.

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-graduação em Ciência da Computação, Florianópolis, 2011 / Made available in DSpace on 2012-10-25T16:36:20Z (GMT). No. of bitstreams: 1 297999.pdf: 9208793 bytes, checksum: d8d5b0071dd3f68c541c93b5e9ce9ec1 (MD5) / Um Data Warehouse Espaço-Temporal (DWET) manipula concomitantemente dados convencionais, espaciais e temporais. Uma necessidade ainda não atendida pela tecnologia de DWET é o suporte à análise de informação de redes complexas de elementos espaciais. Neste sentido, este trabalho propõe um modelo para a análise de redes complexas em DWET. Inspirado em ideias da Geografia, este modelo tem por objetivo representar a estrutura da rede e os estados dos elementos que a compõem, para suportar a análise da evolução do estado de diferentes porções da rede ao longo do tempo. O modelo proposto utiliza ontologias para descrever hierarquias de tipos de elementos da rede, baseadas em conceitualizações específicas do domínio de aplicação, além de ontologias sobre partições do espaço e do tempo. Dimensões de datamarts podem ser geradas a partir de visões dessas ontologias, para contemplar necessidades de análise específicas. O modelo proposto estende um modelo dimensional espaço-temporal para suportar OLAP espacial (SOLAP) com os elementos da rede, usando dimensões de análise definidas de acordo com hierarquias contidas nas ontologias. Ele também define um operador denominado Trace para permitir a análise da evolução do estado dos componentes de porções da rede, selecionadas de acordo com as dimensões de análise definidas para o datamart. O modelo proposto foi implementado em um protótipo. A interface gráfica, baseada em tabelas e mapas, está integrada ao módulo SOLAP. Ao navegar pelos mapas e tabelas apresentando resultados de operações SOLAP, outras operações SOLAP podem ser invocadas e os resultados apresentados em novos gráficos e tabelas. Um slider permite a análise da evolução temporal do estado de porções da rede. Por fim, a modelo é avaliado em um estudo de caso do setor elétrico, o qual possibilita a investigação de padrões e tendências espaço-temporais em diferentes porções de uma rede de distribuição de energia elétrica.