000 03773nam a22003855i 4500
001 297289
003 MX-SnUAN
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007 cr nn 008mamaa
008 150903s2008 gw | o |||| 0|eng d
020 _a9783540744054
_99783540744054
024 7 _a10.1007/9783540744054
_2doi
035 _avtls000350832
039 9 _a201509030501
_bVLOAD
_c201405060241
_dVLOAD
_y201402171107
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA76.9.D3
100 1 _aMalinowski, Elzbieta.
_eautor
_9332957
245 1 0 _aAdvanced Data Warehouse Design :
_bFrom Conventional to Spatial and Temporal Applications /
_cby Elzbieta Malinowski, Esteban Zimányi.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2008.
300 _brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aData-Centric Systems and Applications
500 _aSpringer eBooks
505 0 _ato Databases and Data Warehouses -- Conventional Data Warehouses -- Spatial Data Warehouses -- Temporal Data Warehouses -- Designing Conventional Data Warehouses -- Designing Spatial and Temporal Data Warehouses -- Conclusions and Future Work.
520 _aA data warehouse stores large volumes of historical data required for analytical purposes. This data is extracted from operational databases; transformed into a coherent whole using a multidimensional model that includes measures, dimensions, and hierarchies; and loaded into a data warehouse during the extraction-transformation-loading (ETL) process. Malinowski and Zimányi explain in detail conventional data warehouse design, covering in particular complex hierarchy modeling. Additionally, they address two innovative domains recently introduced to extend the capabilities of data warehouse systems, namely the management of spatial and temporal information. Their presentation covers different phases of the design process, such as requirements specification, conceptual, logical, and physical design. They include three different approaches for requirements specification depending on whether users, operational data sources, or both are the driving force in the requirements gathering process, and they show how each approach leads to the creation of a conceptual multidimensional model. Throughout the book the concepts are illustrated using many real-world examples and completed by sample implementations for Microsoft's Analysis Services 2005 and Oracle 10g with the OLAP and the Spatial extensions. For researchers this book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course. The book may help experienced data warehouse designers to enlarge their analysis possibilities by incorporating spatial and temporal information. Finally, experts in spatial databases or in geographical information systems could benefit from the data warehouse vision for building innovative spatial analytical applications.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aZimányi, Esteban.
_eautor
_9327385
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783540744047
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-540-74405-4
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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