A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions

Abstract

Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called “Racines”, gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration.

Complementarity of vector and raster models in spatial data cubes for risk analysis. Brussels emergency services case study

“…Afin de permettre des interactions, notamment par comparaisons et recoupements, entre cubes de données raster et vectoriels, l'outil est conceptualisé sur base d'un métamodèle unique (Kasprzyk et Devillet, 2021) permettant la navigation dans une constellation de cubes aux dimensions hétérogènes. Dans notre cas d'étude, cette hétérogénéité concerne les dimensions spatiales du SOLAP pouvant être géographiques (vectorielles) ou géométriques (raster).…”

“…Pour fonctionner avec plusieurs cubes de données, un outil SOLAP nécessite la définition des différents concepts manipulés au sein du système : cubes de données, dimensions, dimensions géographiques, hiérarchies, mesures, etc. Cette définition passe par un métamodèle (Boulil et al, 2011 ;Kasprzyk et Devillet, 2021 ;OMG, 2003) qui, une fois implémenté, permet le stockage de toutes les métadonnées des cubes afin de les explorer à travers les opérations SOLAP.…”

A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

“…It demonstrated that data cube descriptive models can be used for the easy integration of heterogeneous data and SOLAP navigation in complex towers of data cubes. In [42], the study proved that the combination of both OLAP and data mining provides excellent solutions. OLAP mining databases enhance data mining and analysis capabilities directly into the database server.…”