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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
18/03/1998 |
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Data da última atualização: |
25/11/2013 |
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Autoria: |
MELO, R. N. |
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Título: |
Tutorial on data warehouse technology. |
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Ano de publicação: |
1997 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS, 12., 1997, Fortaleza. Anais... Fortaleza: UFC, 1997. |
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Páginas: |
p. 8. |
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Idioma: |
Inglês |
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Notas: |
SBBD'97. Editado por Vânia Maria Ponte Vidal. |
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Conteúdo: |
This tutorial provides an overview of the technologies used in building an infrastructure to support a data warehouse and OLAP (On Line Analytical Processing) applications. Participants will clearly understand the architecture of a data warehouse, and the fact that a number of components and processes contribute to the overall product. The tutorial will ensure users understand how OLAP differs from OLTP (On Line Transaction Processing) and the fundamental difference between relational systems and multidimensional systems. We will examine topics including: Data vs information; Operational vs analytical needs; The definitions of OLTP and OLAP; Analytical problems to be handled through multidimensional analysis; Analytical problems to be handled through relational systems (ROLAP); An overview of data warehouse architecture; OLTP database overview; Data from external sources; Extraction and propagation requirements; Transformation and cleansing requirements; Metadata and what it means; Star-join schema for fact tables, measurements, facts, fact records, dimensions, attributes and time; Showflake schema; Data refining; Aggregation and propagation; Data Marts; OLAP tools and presentation tools. In summary, uponcompletion, students should understand the different types of database techniques that support data warehouses and their applications. They should also understand different techniques and tools for extracting data from operational systems to the data warehouse and for building applications on top of the data warehouse. MenosThis tutorial provides an overview of the technologies used in building an infrastructure to support a data warehouse and OLAP (On Line Analytical Processing) applications. Participants will clearly understand the architecture of a data warehouse, and the fact that a number of components and processes contribute to the overall product. The tutorial will ensure users understand how OLAP differs from OLTP (On Line Transaction Processing) and the fundamental difference between relational systems and multidimensional systems. We will examine topics including: Data vs information; Operational vs analytical needs; The definitions of OLTP and OLAP; Analytical problems to be handled through multidimensional analysis; Analytical problems to be handled through relational systems (ROLAP); An overview of data warehouse architecture; OLTP database overview; Data from external sources; Extraction and propagation requirements; Transformation and cleansing requirements; Metadata and what it means; Star-join schema for fact tables, measurements, facts, fact records, dimensions, attributes and time; Showflake schema; Data refining; Aggregation and propagation; Data Marts; OLAP tools and presentation tools. In summary, uponcompletion, students should understand the different types of database techniques that support data warehouses and their applications. They should also understand different techniques and tools for extracting data from operational systems to the data warehouse and for buildi... Mostrar Tudo |
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Palavras-Chave: |
Banco de dados. |
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Thesaurus Nal: |
Databases. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02042naa a2200169 a 4500
001 1006125
005 2013-11-25
008 1997 bl uuuu u00u1 u #d
100 1 $aMELO, R. N.
245 $aTutorial on data warehouse technology.
260 $c1997
300 $ap. 8.
500 $aSBBD'97. Editado por Vânia Maria Ponte Vidal.
520 $aThis tutorial provides an overview of the technologies used in building an infrastructure to support a data warehouse and OLAP (On Line Analytical Processing) applications. Participants will clearly understand the architecture of a data warehouse, and the fact that a number of components and processes contribute to the overall product. The tutorial will ensure users understand how OLAP differs from OLTP (On Line Transaction Processing) and the fundamental difference between relational systems and multidimensional systems. We will examine topics including: Data vs information; Operational vs analytical needs; The definitions of OLTP and OLAP; Analytical problems to be handled through multidimensional analysis; Analytical problems to be handled through relational systems (ROLAP); An overview of data warehouse architecture; OLTP database overview; Data from external sources; Extraction and propagation requirements; Transformation and cleansing requirements; Metadata and what it means; Star-join schema for fact tables, measurements, facts, fact records, dimensions, attributes and time; Showflake schema; Data refining; Aggregation and propagation; Data Marts; OLAP tools and presentation tools. In summary, uponcompletion, students should understand the different types of database techniques that support data warehouses and their applications. They should also understand different techniques and tools for extracting data from operational systems to the data warehouse and for building applications on top of the data warehouse.
650 $aDatabases
653 $aBanco de dados
773 $tIn: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS, 12., 1997, Fortaleza. Anais... Fortaleza: UFC, 1997.
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Embrapa Agricultura Digital (CNPTIA) |
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