Spatial OLAP and Map Generalization

2012 ◽  
Vol 8 (1) ◽  
pp. 24-51 ◽  
Author(s):  
Sandro Bimonte ◽  
Michela Bertolotto ◽  
Jérôme Gensel ◽  
Omar Boussaid
Keyword(s):  
Data Warehouse ◽  
Spatial Data ◽  
Multidimensional Analysis ◽  
Central Component ◽  
Mental Process ◽  
Map Generalization ◽  
Spatial Decision Support ◽  
Spatial Decision Support Systems ◽  
Spatial Dimensions ◽  
And Algebra

Map generalization can be used as a central component of Spatial Decision Support Systems to provide a simplified and more readable cartographic visualization of geographic information. Indeed, it supports the user mental process for discovering important and unknown geospatial relations, trends and patterns. Spatial OLAP (SOLAP) integrates spatial data into OLAP and data warehouse systems. SOLAP models and tools are based on the concepts of spatial dimensions and measures that represent the axes and the subjects of the spatio-multidimensional analysis. Although powerful under some respect, current SOLAP models cannot support map generalization capabilities. This paper provides the first effort to integrate Map Generalization and OLAP. Firstly the authors define all modeling and querying requirements to do this integration, and then present a SOLAP model and algebra that support map generalization concepts. The approach extends SOLAP spatial hierarchies introducing multi-association relationships, supports imprecise measures, and it takes into account spatial dimensions constraints generated by multiple map generalization hierarchies.

Enhancing Decision Support Systems with Spatial Capabilities

2011 ◽  
pp. 2542-2557
Author(s):  
Marcus Costa Sampaio ◽  
Cláudio de Souza Baptista ◽  
André Gomes de Sousa ◽  
Fabiana Ferreira do Nascimento
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Spatial Data ◽  
Support Systems ◽  
Data Cube ◽  
Related Data ◽  
Spatial Dimensions ◽  
Object Relational ◽  
Long Time

This chapter introduces spatial dimensions and measures as a means of enhancing decision support systems with spatial capabilities. By some way or other, spatial related data has been used for a long time; however, spatial dimensions have not been fully exploited. It is presented a data model that tightly integrates data warehouse and geographical information systems — so characterizing a spatial data warehouse (SDW) — ; more precisely, the focus is on a formalization of SDW concepts, on a spatial-aware data cube using object-relational technology, and on issues underlying a SDW — specially regarding spatial data aggregation operations. Finally, the MapWarehouse prototype is presented aiming to validate the ideas proposed. The authors believe that SDW allows for the efficient processing of queries that use, jointly, spatial and numerical temporal data (e.g., temporal series from summarized spatial and numerical measures).

A Web-Based Tool for Spatio-Multidimensional Analysis of Geographic and Complex Data

2012 ◽  
pp. 32-58
Author(s):  
Sandro Bimonte
Keyword(s):  
Spatial Data ◽  
Venice Lagoon ◽  
Environmental Data ◽  
Multidimensional Analysis ◽  
Complex Data ◽  
Web Based ◽  
Complex Information ◽  
Semantic Component ◽  
Spatial Dimensions ◽  
Spatial Data Warehouses

Spatial OLAP (SOLAP) integrates spatial data into OLAP systems, and SOLAP models define spatial dimensions while measuring spatio-multidimensional operators. In this paper, the author presents the concepts of geographic and complex measures that allow integrating geographic and complex information as subjects of analysis in spatial data warehouses. The concept of geographic measure extends the concept of spatial measure to the semantic component of geographic information. The concept of complex measure allows introducing complex data as subjects of multidimensional analysis. To reduce the gap in flexibility between spatial and multidimensional analysis, this paper proposes a symmetrical representation of measures and dimensions. Additionally, the author presents a Web-based SOLAP prototype, GeWOlap, that enriches existing SOLAP tools by effectively and easily supporting symmetrical geographic/complex measures and dimensions for modeling and visualization. To validate this approach, the simulated environmental data concerning the pollution of the Venice lagoon is used.

GIS as Spatial Decision Support Systems

2011 ◽  
pp. 151-174 ◽  
Author(s):  
Suprasith Jarupathirun ◽  
Fatemeh Zahedi
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Intelligent Agents ◽  
Support Systems ◽  
Decision Aids ◽  
Geographic Information ◽  
Spatial Decision Support ◽  
Spatial Decision Support Systems ◽  
Decision Tools ◽  
Spatial Dimensions

This chapter discusses the use of geographic information systems (GIS) for spatial decision support systems (SDSS). It argues that the increased availability in spatial business data has created new opportunities for the use of GIS in creating decision tools for use in a variety of decisions that involve spatial dimensions. This chapter identifies visualization and analytical capabilities of GIS that make such systems uniquely appropriate as decision aids, and presents a conceptual model for measuring the efficacy of GIS-based SDSS. The discussions on the applications of SDSS and future enhancements using intelligent agents are intended to inform practitioners and researchers of the opportunities for the enhancement and use of such systems.

Enhancing Decision Support Systems with Spatial Capabilities

2011 ◽  
pp. 94-116
Author(s):  
Marcus Costa Sampaio ◽  
Cláudio de Souza Baptita ◽  
André Gomes de Sousa ◽  
Fabiana Ferreira do Nascimento
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Spatial Data ◽  
Support Systems ◽  
Data Cube ◽  
Related Data ◽  
Spatial Dimensions ◽  
Object Relational ◽  
Long Time

This chapter introduces spatial dimensions and measures as a means of enhancing decision support systems with spatial capabilities. By some way or other, spatial related data has been used for a long time; however, spatial dimensions have not been fully exploited. It is presented a data model that tightly integrates data warehouse and geographical information systems — so characterizing a spatial data warehouse (SDW) — ; more precisely, the focus is on a formalization of SDW concepts, on a spatial-aware data cube using object-relational technology, and on issues underlying a SDW — specially regarding spatial data aggregation operations. Finally, the MapWarehouse prototype is presented aiming to validate the ideas proposed. The authors believe that SDW allows for the efficient processing of queries that use, jointly, spatial and numerical temporal data (e.g., temporal series from summarized spatial and numerical measures).

Spatial Decision Support Systems

2011 ◽  
pp. 28-39 ◽  
Author(s):  
Peter B. Keenan
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Future Development ◽  
Spatial Data ◽  
Support Systems ◽  
Decision Makers ◽  
Spatial Decision Support ◽  
Spatial Decision Support Systems ◽  
Spatial Systems ◽  
Public Data

Many types of challenging problems faced by decision makers have a geographic or spatial component. Spatial decision support systems (SDSS) can effectively support this class of problem. This represents a growing class of DMSS, taking advantage of the increasing capability of technology to deal with spatial data. SDSS is characterized by the use of significant amounts of public data external to the organizations that use it and the increasing availability of such spatial data facilities wider use of such systems. This chapter describes spatial systems, their history, their relationship to other systems, their mean areas of application and their future development.

A Web-Based Tool for Spatio-Multidimensional Analysis of Geographic and Complex Data

2010 ◽  
Vol 1 (2) ◽  
pp. 42-67 ◽  
Author(s):  
Sandro Bimonte
Keyword(s):  
Spatial Data ◽  
Venice Lagoon ◽  
Environmental Data ◽  
Multidimensional Analysis ◽  
Complex Data ◽  
Web Based ◽  
Complex Information ◽  
Semantic Component ◽  
Spatial Dimensions ◽  
Spatial Data Warehouses

Spatial OLAP (SOLAP) integrates spatial data into OLAP systems, and SOLAP models define spatial dimensions while measuring spatio-multidimensional operators. In this paper, the author presents the concepts of geographic and complex measures that allow integrating geographic and complex information as subjects of analysis in spatial data warehouses. The concept of geographic measure extends the concept of spatial measure to the semantic component of geographic information. The concept of complex measure allows introducing complex data as subjects of multidimensional analysis. To reduce the gap in flexibility between spatial and multidimensional analysis, this paper proposes a symmetrical representation of measures and dimensions. Additionally, the author presents a Web-based SOLAP prototype, GeWOlap, that enriches existing SOLAP tools by effectively and easily supporting symmetrical geographic/complex measures and dimensions for modeling and visualization. To validate this approach, the simulated environmental data concerning the pollution of the Venice lagoon is used.

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