Reclassification queries in a geographical data warehouse
A 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 groupe...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2010
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udem.edu.co:11407/3403
- Acceso en línea:
- http://hdl.handle.net/11407/3403
- Palabra clave:
- Temporal data warehouses
Spatial data warehouses
OLAP
Members’ reclassification
Season queries
Bodegas de datos temporales
Bodegas de datos espaciales
OLAP
Reclasificación de miembros
Consultas de temporadas
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Reclassification queries in a geographical data warehouse Consultas de temporada espacial en un modelo multidimensional |
| title |
Reclassification queries in a geographical data warehouse |
| spellingShingle |
Reclassification queries in a geographical data warehouse Temporal data warehouses Spatial data warehouses OLAP Members’ reclassification Season queries Bodegas de datos temporales Bodegas de datos espaciales OLAP Reclasificación de miembros Consultas de temporadas |
| title_short |
Reclassification queries in a geographical data warehouse |
| title_full |
Reclassification queries in a geographical data warehouse |
| title_fullStr |
Reclassification queries in a geographical data warehouse |
| title_full_unstemmed |
Reclassification queries in a geographical data warehouse |
| title_sort |
Reclassification queries in a geographical data warehouse |
| dc.subject.spa.fl_str_mv |
Temporal data warehouses Spatial data warehouses OLAP Members’ reclassification Season queries Bodegas de datos temporales Bodegas de datos espaciales OLAP Reclasificación de miembros Consultas de temporadas |
| topic |
Temporal data warehouses Spatial data warehouses OLAP Members’ reclassification Season queries Bodegas de datos temporales Bodegas de datos espaciales OLAP Reclasificación de miembros Consultas de temporadas |
| description |
A 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. |
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2010 |
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2010 |
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2017-06-15T22:05:18Z |
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2017-06-15T22:05:18Z |
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Article |
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Moreno, 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). |
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02540770 |
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http://hdl.handle.net/11407/3403 |
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Moreno, 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). 02540770 |
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http://hdl.handle.net/11407/3403 |
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eng |
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eng |
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http://produccioncientificaluz.org/index.php/tecnica/article/view/6714/6701 |
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Revista Técnica de la Facultad de Ingeniería. Vol. 33, Nº 3, 263 - 271, 2010 |
| dc.relation.references.spa.fl_str_mv |
Inmon W. H.: “Building the Data Warehouse”, Wiley, New York, 2005. Kimball R., Ross M., Thornthwaite W., Mundy J., and Becker B.: “The Data Warehouse Lifecycle Toolkit”, Wiley, New York, 2008. Agrawal R., Gupta A., and Sarawagi S.: “Modeling multidimensional databases”. 13th ICDE’97, Birmingham (1997) 232-243. Gyssens 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. Golfarelli M., and Rizzi S.: “A methodological framework for data warehouse design”. 1st DOLAP, Washington D.C. (1998) 3-9. Lehner W., Albrecht J., and Wedekind H.: “Normal forms for multidimensional databases”, 10th SSDBM’98, Capri (1998) 63-72. Pedersen 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. Jensen C. S., Kligys A., Pedersen T. B., and Timko I.: “Multidimensional data modeling for location-based services”. 10th GIS 2002, McLean (2002) 55-61. Timko 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. Kumar 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. Jarke M., Lenzerini M., Vassiliou Y., and Vassiliadis P.: “Fundamentals of Data Warehouses”, Springer, New York, 2003. Torlone R.: Conceptual multidimensional models. In: M. Rafanelli (ed), Multidimensional Databases: Problems and Solutions. Idea Group, USA(2003), 69-90. Moreno F., Arango F., and Fileto R.: “Season queries on a temporal multidimensional model”. 11th IM2, Valencia (2009) to appear. Malinowski E., Zimányi E.: “Advanced Data Warehouse Design: from Conventional to Spatial and Temporal Applications”, Springer, New York, 2008. Golfarelli M., and Rizzi S.: “A survey on temporal data warehousing”. International Journal of Data Warehousing and Mining, Vol. 5, No. 1 (2009), 1-17. Rao 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. Shekhar 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. Chamoni P., Stock S.: “Temporal structures in data warehousing”. 1st DaWaK, Florence(1999) 353-358. Mendelzon A., and Vaisman A.: “Temporal queries in OLAP”. 26th VLDB, Cairo (2000) 242-253. Malinowski E., Zimányi E.: “A conceptual solution for representing time in data warehouse dimensions”. 3rd APCCM 2006, Hobart (2006) 45-54. Moreno F., Arango F., and Fileto R.: “A multigranular temporal multidimensional model”. 1st miproBIS, Opatija (2009) 1-6. Parent C., Spaccapietra S., and Zimányi E.: “Spatio-temporal conceptual models: data structures + space + time”. 7th ACM-GIS, Kansas (1999) 26-33. Schneider M.: “Computing the topological relationship of complex regions”. 15th DEXA, Zaragoza (2004) 844-853. Datta 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. Whitehorn M., Zare R., and Pasumansky M.: “Fast Track to MDX”, Springer, New York, 2006. Brakatsoulas S., Pfoser D., and Tryfona N.: “Modeling, storing, and mining moving object databases”. 8th IDEAS, Coimbra (2004) 68-77. Orlando 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. Marketos G., Frentzos E., Ntoutsi I, Pelekis N., Raffaetà A., and Theodoridis Y.: “Building real world trajectory warehouses”. 7th MobiDE’08, Vancouver (2008) 1-8. Spaccapietra 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. |
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Universidad del Zulia |
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Ingeniería de Sistemas |
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Facultad de Ingenierías |
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Revista Técnica de la Facultad de Ingeniería |
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2017-06-15T22:05:18Z2017-06-15T22:05:18Z2010Moreno, 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).02540770http://hdl.handle.net/11407/3403A 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.Una 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.engUniversidad del ZuliaIngeniería de SistemasFacultad de Ingenieríashttp://produccioncientificaluz.org/index.php/tecnica/article/view/6714/6701Revista Técnica de la Facultad de Ingeniería. Vol. 33, Nº 3, 263 - 271, 2010Inmon W. H.: “Building the Data Warehouse”, Wiley, New York, 2005.Kimball R., Ross M., Thornthwaite W., Mundy J., and Becker B.: “The Data Warehouse Lifecycle Toolkit”, Wiley, New York, 2008.Agrawal R., Gupta A., and Sarawagi S.: “Modeling multidimensional databases”. 13th ICDE’97, Birmingham (1997) 232-243.Gyssens 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.Golfarelli M., and Rizzi S.: “A methodological framework for data warehouse design”. 1st DOLAP, Washington D.C. (1998) 3-9.Lehner W., Albrecht J., and Wedekind H.: “Normal forms for multidimensional databases”, 10th SSDBM’98, Capri (1998) 63-72.Pedersen 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.Jensen C. S., Kligys A., Pedersen T. B., and Timko I.: “Multidimensional data modeling for location-based services”. 10th GIS 2002, McLean (2002) 55-61.Timko 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.Kumar 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.Jarke M., Lenzerini M., Vassiliou Y., and Vassiliadis P.: “Fundamentals of Data Warehouses”, Springer, New York, 2003.Torlone R.: Conceptual multidimensional models. In: M. Rafanelli (ed), Multidimensional Databases: Problems and Solutions. Idea Group, USA(2003), 69-90.Moreno F., Arango F., and Fileto R.: “Season queries on a temporal multidimensional model”. 11th IM2, Valencia (2009) to appear.Malinowski E., Zimányi E.: “Advanced Data Warehouse Design: from Conventional to Spatial and Temporal Applications”, Springer, New York, 2008.Golfarelli M., and Rizzi S.: “A survey on temporal data warehousing”. International Journal of Data Warehousing and Mining, Vol. 5, No. 1 (2009), 1-17.Rao 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.Shekhar 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.Chamoni P., Stock S.: “Temporal structures in data warehousing”. 1st DaWaK, Florence(1999) 353-358.Mendelzon A., and Vaisman A.: “Temporal queries in OLAP”. 26th VLDB, Cairo (2000) 242-253.Malinowski E., Zimányi E.: “A conceptual solution for representing time in data warehouse dimensions”. 3rd APCCM 2006, Hobart (2006) 45-54.Moreno F., Arango F., and Fileto R.: “A multigranular temporal multidimensional model”. 1st miproBIS, Opatija (2009) 1-6.Parent C., Spaccapietra S., and Zimányi E.: “Spatio-temporal conceptual models: data structures + space + time”. 7th ACM-GIS, Kansas (1999) 26-33.Schneider M.: “Computing the topological relationship of complex regions”. 15th DEXA, Zaragoza (2004) 844-853.Datta 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.Whitehorn M., Zare R., and Pasumansky M.: “Fast Track to MDX”, Springer, New York, 2006.Brakatsoulas S., Pfoser D., and Tryfona N.: “Modeling, storing, and mining moving object databases”. 8th IDEAS, Coimbra (2004) 68-77.Orlando 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.Marketos G., Frentzos E., Ntoutsi I, Pelekis N., Raffaetà A., and Theodoridis Y.: “Building real world trajectory warehouses”. 7th MobiDE’08, Vancouver (2008) 1-8.Spaccapietra 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.Revista Técnica de la Facultad de IngenieríaTemporal data warehousesSpatial data warehousesOLAPMembers’ reclassificationSeason queriesBodegas de datos temporalesBodegas de datos espacialesOLAPReclasificación de miembrosConsultas de temporadasReclassification queries in a geographical data warehouseConsultas de temporada espacial en un modelo multidimensionalArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Moreno, Francisco J.Echeverri, JaimeArango, FernandoMoreno, Francisco J.; Universidad Nacional de ColombiaEcheverri, Jaime; Universidad de MedellínArango, Fernando; Universidad Nacional de ColombiaORIGINALArticulo.htmltext/html513http://repository.udem.edu.co/bitstream/11407/3403/1/Articulo.html51fa31c709729792a4c9a7c22db787b4MD5111407/3403oai:repository.udem.edu.co:11407/34032020-05-27 15:45:26.03Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co |