When Spatial Analysis Meets OLAP

Abstract

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Existing SOLAP models do not completely integrate the semantic component of geographic information (alphanumeric attributes and relationships) or the flexibility of spatial analysis into multidimensional analysis. In this paper, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCube’s algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this paper, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.

Multidimensional modeling and analysis of large and complex watercourse data: an OLAP-based solution

“…), (2) linking the data cubes to each other to allow drill-across OLAP operations, to answer the questions related to interdependencies between the water quality parameters such as the influence of physicochemical state of water bodies in the appearance/ disappearance of faunal and floristic specifies, the influence of hydromorphological characteristics of water bodies in their physicochemical states, etc. Another perspective is to study the possibilities offered by spatial OLAP tools (Bédard et al, 2007;Miquel et al, 2010). These tools may help end users to understand water quality data better by allowing map visualizations and explorations of data.…”

“…A number of studies apply (S)OLAP to decision support in domains such as marketing, public health monitoring, transportation planning, agriculture, environmental risk management, etc. (Bédard et al, 2007;Miquel et al, 2010). However, only Chen et al (2007), McGuire and Gangopadhyay (2006), and Wang and Guo (2013) investigate this technology in the specific domain of water quality management.…”

Spatial OLAP and Map Generalization

“…Our work is now directed towards the support for the cartographic visualization of our model using the prototype GeWOlap (Bimonte et al, 2010) as visualization is mandatory for an effective SOLAP analysis (Raffaetà et al, 2011).…”

“…Integration of map generalization into SOLAP models could improve the analysis capabilities of spatio-multidimensional operators and also greatly improve the visual component of SOLAP tools (Bédard et al, 2002) by allowing adaptive zoom in/out operations (Cecconi, 2003) on spatial dimensions e.g., adjustment of maps representing spatial dimensions, its contents and the symbolization to target scale in consequence of a zooming operation. Unfortunately, most existing SOLAP models (Ahmed et al, 2006;Damiani et al, 2006;Fidalgo et al, 2004;Glorio et al, 2008;Gomez et al, 2009;Jensen et al, 2004;Malinoswky et al, 2008;Pourrabas, 2001;Sampaio et al, 2006;Raffaetà et al, 2011), and consequently SOLAP tools (Bimonte et al, 2010;Raffaetà et al, 2011), do not integrate map generalization on complex hierarchies generated by map generalization operators and integrity constraints which define rules to control the multidimensional exploration process.…”

Semi-Automatic Design of Spatial Data Cubes from Simulation Model Results

“…Using SOLAP technologies for analyzing simulation results implies to build different data schemas with several dimensions corresponding to analysis axes. From these schemas, one must generate spatial data cubes on which SOLAP operations are performed allowing decisionmakers to visualize indicators by means of interactive maps, pivot tables and graphic displays (Bimonte et al, 2010). However, designing and implementing multidimensional schemas are time-consuming tasks, requiring computer science experts (Phipps & Davis, 2002).…”