Mostrar el registro sencillo del ítem

Consultas de temporada espacial en un modelo multidimensional

dc.creatorMoreno, Francisco J.spa
dc.creatorEcheverri, Jaimespa
dc.creatorArango, Fernandospa
dc.date.accessioned2017-06-15T22:05:18Z
dc.date.available2017-06-15T22:05:18Z
dc.date.created2010
dc.identifier.citationMoreno, F., Echeverri, J., & Arango, F. (2010). Reclassification queries in a geographical data warehouse. Revista Técnica de la Facultad de Ingeniería. Universidad del Zulia, 33(3).spa
dc.identifier.issn02540770
dc.identifier.urihttp://hdl.handle.net/11407/3403
dc.descriptionA data warehouse is a specialized database designed to support decision-making and is usually modeled using a multidimensional view of data. A multidimensional model includes dimensions that are composed of levels. The levels of a dimension are organized in a hierarchy, e.g., salespersons are grouped into stores. Throughout its lifespan a member (instance) of a level can be associated with several members of a higher level of the hierarchy, e.g., the salespersons can rotate between the stores. This succession of associations enables the formulation of queries such as: “How much did a salesperson sell in his n-th season (stay) in the store X?” In this paper, we enrich this type of query, known as season queries, with spatial features. This enhancement enables the formulation of queries such as: “How much did a salesperson sell in his n-th season in a given geographic region?” (A spatial query window that contains a set of stores.) In order to facilitate their formulation, we propose and incorporate an operator into a multidimensional query language to demonstrate their feasibility of implementation.spa
dc.descriptionUna bodega de datos es una base de datos especialmente diseñada para soportar la toma de decisiones y es usualmente modelada en forma multidimensional. Un modelo multidimensional posee dimensiones las cuales se componen de niveles. Los niveles de una dimensión se organizan jerárquicamente, e.g., los vendedores se agrupan en tiendas. A través de su existencia un miembro (instancia) de un nivel se puede asociar con varios miembros pertenecientes a un nivel superior en la jerarquía, e.g., los vendedores pueden rotar entre las tiendas. Esta sucesión de asociaciones posibilita la formulación de consultas como: “Cuánto vendió un vendedor en su enésima temporada (estadía) en la tienda X?” En este artículo, se enriquece este tipo de consultas, conocidas como consultas de temporadas, con aspectos espaciales. Esta mejora posibilita la formulación de consultas como: “Cuánto vendió un vendedor en su enésima temporada (estadía) en una región geográfica” (Una región espacial que cubre un conjunto de tiendas.) Para facilitar su formulación, se propone e incorpora un operador en un lenguaje de consulta multidimensional para demostrar la viabilidad de su implementación.spa
dc.language.isoeng
dc.publisherUniversidad del Zuliaspa
dc.relation.isversionofhttp://produccioncientificaluz.org/index.php/tecnica/article/view/6714/6701spa
dc.sourceRevista Técnica de la Facultad de Ingenieríaspa
dc.subjectTemporal data warehousesspa
dc.subjectSpatial data warehousesspa
dc.subjectOLAPspa
dc.subjectMembers’ reclassificationspa
dc.subjectSeason queriesspa
dc.subjectBodegas de datos temporalesspa
dc.subjectBodegas de datos espacialesspa
dc.subjectOLAPspa
dc.subjectReclasificación de miembrosspa
dc.subjectConsultas de temporadasspa
dc.titleReclassification queries in a geographical data warehousespa
dc.titleConsultas de temporada espacial en un modelo multidimensionalspa
dc.typeArticleeng
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.publisher.programIngeniería de Sistemasspa
dc.publisher.facultyFacultad de Ingenieríasspa
dc.creator.affiliationMoreno, Francisco J.; Universidad Nacional de Colombiaspa
dc.creator.affiliationEcheverri, Jaime; Universidad de Medellínspa
dc.creator.affiliationArango, Fernando; Universidad Nacional de Colombiaspa
dc.relation.ispartofesRevista Técnica de la Facultad de Ingeniería. Vol. 33, Nº 3, 263 - 271, 2010spa
dc.relation.referencesInmon W. H.: “Building the Data Warehouse”, Wiley, New York, 2005.spa
dc.relation.referencesKimball R., Ross M., Thornthwaite W., Mundy J., and Becker B.: “The Data Warehouse Lifecycle Toolkit”, Wiley, New York, 2008.spa
dc.relation.referencesAgrawal R., Gupta A., and Sarawagi S.: “Modeling multidimensional databases”. 13th ICDE’97, Birmingham (1997) 232-243.spa
dc.relation.referencesGyssens M., and Lakshmanan L.: “A foundation for multi-dimensional databases”. 23rd VLDB’97, Athens (1997) 106-115.Vassiliadis P.: “Modeling multidimensional databases, cubes and cube operations”. 10th SSDBM, Capri (1998) 53-62.spa
dc.relation.referencesGolfarelli M., and Rizzi S.: “A methodological framework for data warehouse design”. 1st DOLAP, Washington D.C. (1998) 3-9.spa
dc.relation.referencesLehner W., Albrecht J., and Wedekind H.: “Normal forms for multidimensional databases”, 10th SSDBM’98, Capri (1998) 63-72.spa
dc.relation.referencesPedersen T. B., Jensen C. S., and Dyreson C. E.: “A foundation for capturing and querying complex multidimensional data”. Information Systems, Vol. 26, No. 5 (2001) 383-423.spa
dc.relation.referencesJensen C. S., Kligys A., Pedersen T. B., and Timko I.: “Multidimensional data modeling for location-based services”. 10th GIS 2002, McLean (2002) 55-61.spa
dc.relation.referencesTimko I., Dyreson C. E., and Pedersen T. B.: “Probabilistic Data Modeling and Querying for Location-based Data Warehouses”. 17th SSDBM, Santa Barbara (2005) 273-282.spa
dc.relation.referencesKumar N., Gangopadhyay A., Bapna S., Karabatis G., and Chen Z.: “Measuring interestingness of discovered skewed patterns in data cubes”. Decision Support Systems, Vol. 46, No. 1 (2008), 429-439.spa
dc.relation.referencesJarke M., Lenzerini M., Vassiliou Y., and Vassiliadis P.: “Fundamentals of Data Warehouses”, Springer, New York, 2003.spa
dc.relation.referencesTorlone R.: Conceptual multidimensional models. In: M. Rafanelli (ed), Multidimensional Databases: Problems and Solutions. Idea Group, USA(2003), 69-90.spa
dc.relation.referencesMoreno F., Arango F., and Fileto R.: “Season queries on a temporal multidimensional model”. 11th IM2, Valencia (2009) to appear.spa
dc.relation.referencesMalinowski E., Zimányi E.: “Advanced Data Warehouse Design: from Conventional to Spatial and Temporal Applications”, Springer, New York, 2008.spa
dc.relation.referencesGolfarelli M., and Rizzi S.: “A survey on temporal data warehousing”. International Journal of Data Warehousing and Mining, Vol. 5, No. 1 (2009), 1-17.spa
dc.relation.referencesRao F., Zhang L., Yu X., Li Y., and Chen Y.: “Spatial hierarchy and OLAP-favored search in spatial data warehouse”. 6th DOLAP, New Orleans (2003), 48-55.spa
dc.relation.referencesShekhar S., Lu C. T., Tan X., and Chawla S.: Map cube: a visualization tool for spatial data warehouses. In: H. J. Miller, J. Han (eds), Geographic Data Mining and Knowledge Discovery. Taylor and Francis, USA(2001), 73-108.spa
dc.relation.referencesChamoni P., Stock S.: “Temporal structures in data warehousing”. 1st DaWaK, Florence(1999) 353-358.spa
dc.relation.referencesMendelzon A., and Vaisman A.: “Temporal queries in OLAP”. 26th VLDB, Cairo (2000) 242-253.spa
dc.relation.referencesMalinowski E., Zimányi E.: “A conceptual solution for representing time in data warehouse dimensions”. 3rd APCCM 2006, Hobart (2006) 45-54.spa
dc.relation.referencesMoreno F., Arango F., and Fileto R.: “A multigranular temporal multidimensional model”. 1st miproBIS, Opatija (2009) 1-6.spa
dc.relation.referencesParent C., Spaccapietra S., and Zimányi E.: “Spatio-temporal conceptual models: data structures + space + time”. 7th ACM-GIS, Kansas (1999) 26-33.spa
dc.relation.referencesSchneider M.: “Computing the topological relationship of complex regions”. 15th DEXA, Zaragoza (2004) 844-853.spa
dc.relation.referencesDatta A., and Thomas H.: “The cube data model: a conceptual model and algebra for on-line analytical processing in data warehouses”. Decision Support Systems, Vol. 27, No.3 (1999), 289-301.spa
dc.relation.referencesWhitehorn M., Zare R., and Pasumansky M.: “Fast Track to MDX”, Springer, New York, 2006.spa
dc.relation.referencesBrakatsoulas S., Pfoser D., and Tryfona N.: “Modeling, storing, and mining moving object databases”. 8th IDEAS, Coimbra (2004) 68-77.spa
dc.relation.referencesOrlando S., Orsini R., Raffaeta A., and Roncato A.: “Trajectory data warehouses: design and implementation issues”. Journal of Computing Science and Engineering, Vol. 1, No. 2 (2007), 211-232.spa
dc.relation.referencesMarketos G., Frentzos E., Ntoutsi I, Pelekis N., Raffaetà A., and Theodoridis Y.: “Building real world trajectory warehouses”. 7th MobiDE’08, Vancouver (2008) 1-8.spa
dc.relation.referencesSpaccapietra S., Parent C., Damiani M. L., Fernandes de Macêdo J. A., Porto F., and Vangenot C.: “A conceptual view on trajectories”. Data & Knowledge Engineering, Vol. 65, No. 1 (2008), 126-146.spa
dc.type.driverinfo:eu-repo/semantics/article


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem