GeoDWFrame: A Framework for Guiding the Design of Geographical Dimensional Schemas

Fidalgo, Robson; Times, Valéria Cesário; Silva, Joel da; Souza, Fernando da Fonseca de

doi:10.1007/978-3-540-30076-2_3

Cited by 43 publications

(49 citation statements)

References 6 publications

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“…Further on, the same authors present a method to transform a conceptual schema to a logical one, expressed in the Object-Relational paradigm [31]. Fidalgo et al [11] and da Silva et al [46] introduced GeoDWFrame, a framework for spatial OLAP, which classifies dimensions as geographic and hybrid, if they represent only geographic data, or geographic and nonspatial data, respectively. Over this framework, da Silva et al [47] propose GeoMDQL, a query language based on MDX and OGC 6 simple features, for querying spatial data cubes.…”

Section: Article In Pressmentioning

confidence: 99%

Spatial aggregation: Data model and implementation

Gómez

Haesevoets²,

Kuijpers

et al. 2009

Information Systems

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a b s t r a c tData aggregation in Geographic Information Systems (GIS) is a desirable feature, only marginally present in commercial systems nowadays, mostly through ad hoc solutions. We address this problem introducing a formal model that integrates, in a natural way, geographic data and non-spatial information contained in a data warehouse external to the GIS. This approach allows both aggregation of geometric components and aggregation of measures associated to those components, defined in GIS fact tables. We define the notion of geometric aggregation, a general framework for aggregate queries in a GIS setting. Although general enough to express a wide range of (aggregate) queries, some of these queries can be hard to compute in a real-world GIS environment because they involve computing an integral over a certain area. Thus, we identify the class of summable queries, which can be efficiently evaluated replacing this integral with a sum of functions of geometric objects. Integration of GIS and OLAP (On Line Analytical Processing) is supported also through a language, GISOLAP-QL. We present an implementation, denoted Piet, which supports four kinds of queries: standard GIS, standard OLAP, geometric aggregation (like ''total population in states with more than three airports''), and integrated GIS-OLAP queries (''total sales by product in cities crossed by a river'', also allowing navigation of the results). Further, Piet implements a novel query processing technique: first, a process called subpolygonization decomposes each thematic layer in a GIS, into open convex polygons; then, another process (the overlay precomputation) computes and stores in a database the overlay of those layers for later use by a query processor. Experimental evaluation showed that for a wide class of geometric queries, overlay precomputation outperforms R-tree-based techniques, suggesting that it can be an alternative for GIS query processing.

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Section: Article In Pressmentioning

confidence: 99%

Spatial aggregation: Data model and implementation

Gómez

Haesevoets²,

Kuijpers

et al. 2009

Information Systems

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“…SOLAP fundamental concepts, historical perspective, and extensive references can be found in Rivest et al 2001, Bedard et al 2005, Tchounikine et al 2005, Ferri et al 2002, while SOLAP prototypes are described by Kouba et al 2000, Stefanovic et al 2000, Ferreira et al 2001, Shekhar et al 2001, Fidalgo et al 2004, Bedard et al 2005, Scotch and Parmato 2005, Silva et al 2005, among others.…”

Section: Solap Analysis Technique For Spatial Datamentioning

confidence: 99%

Active Transportation, Neighborhood Planning and Participatory GIS (Geographic Information System)

Schlossberg

Larco²

2008

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On the Cover:A common and humorous test of mental dexterity is the ability to walk and chew gum at the same time. These two simple tasks rarely conflict and are performed without thought. Over the past 10 years a new test of mental dexterity has arisen with vast implications in the field of geospatial technology; can one walk and collect data at the same time?Research on walking and the built environment has accelerated by an increased interest in the relationship between urban form and public health. As the research has progressed, so has the interest in developing ways to collect data at a very fine scale-in essence, to be able to collect data at the streetscape level and link this data to transportation behavior. An article by Marc Schlossberg, Asha Weinstein, and Katja Irvin entitled An Assessment of GISenabled Walkability Audits examines the development and implementation of a GIS-based pedestrian audit tool that allows users to collect data in electronic form using a handheld computer. The paper, which highlights Volume 19, Number 2 of the URISA Journal, identifies current shortcomings with walkability audit tools but concludes that appropriately applying GIS-enabled pedestrian audit tools can be an efficient way to collect and quickly analyze pedestrian infrastructure characteristics so that planners, practitioners, policy makers, and community members can make more effective decisions. SUBMISSIONS: This publication accepts from authors an exclusive right of first publication to their article plus an accompanying grant of nonexclusive full rights. The publisher requires that full credit for first publication in the URISA Journal is provided in any subsequent electronic or print publications. For more information, the "Manuscript Submission Guidelines for Refereed Articles" is available on our website, www.urisa. org, or by calling (847) 824-6300. SUBSCRIPTION AND ADVERTISING: All correspondence about advertising, subscriptions, and URISA memberships should be directed to: Urban and Regional Information Systems Association, 1460 Renaissance Dr., Suite 305, Park Ridge, Illinois, 60068-1348; Voice (847) 824-6300; Fax (847) 824-6363; E-mail info@urisa.org.URISA Journal is published two times a year by the Urban and Regional Information Systems Association.© 2007 by the Urban and Regional Information Systems Association. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by permission of the Urban and Regional Information Systems Association.Educational programs planned and presented by URISA provide attendees with relevant and rewarding continuing education experience. However, neither the content (whether written or oral) of any course, seminar, or other presentation, nor the use of a specific product in conjunction therewith, nor the exhibition of any materials by any party coincident with the educational event, should be construed as indicating endorsement or approval of the views presented, the products used, or the materials exhi...

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“…A relational SDW inherits several components of conventional data warehouses, such as fact and dimension tables, numeric measures and hierarchies that aggregate these measures according to distinct granularity levels [1]. Additionally, the SDW has spatial attributes that store vector geometries and define spatially-enabled components, such as spatial dimension tables, spatial measures and spatial hierarchies [2][3] [4]. Typically, a spatial hierarchy is a predefined 1:N association among higher and lower granularity spatial attributes that is determined by databases that does not support indices nor materialized views [7].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Spatial Data Warehouses

Siqueira

Ciferri

Times

et al. 2010

Data Warehousing and Knowledge Discovery

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Abstract. Spatial data warehouses (SDW) enable analytical multidimensional queries together with spatial analysis. Mainly, three operations are related to SDW query processing performance: (i) joining large fact tables and large spatial and non-spatial dimension tables; (ii) computing one or more costly spatial predicates based on spatial ad hoc query windows; and (iii) aggregating data according to different spatial granularity levels. Several techniques to improve the query processing performance over SDW have been proposed in the literature. However, we identified the lack of a benchmark to carry out a controlled experimental evaluation of such techniques and, principally, to effectively measure the costs of the aforementioned three complex operations. In this paper, we propose a novel spatial data warehouse benchmark, called Spadawan, to provide performance evaluation environments for SDW and enable a further investigation on spatial data redundancy. The Spadawan benchmark is available at http://gbd.dc.ufscar.br/spadawan.

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GeoDWFrame: A Framework for Guiding the Design of Geographical Dimensional Schemas

Cited by 43 publications

References 6 publications

Spatial aggregation: Data model and implementation

Spatial aggregation: Data model and implementation

Active Transportation, Neighborhood Planning and Participatory GIS (Geographic Information System)

Benchmarking Spatial Data Warehouses

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