When Spatial Analysis Meets OLAP

International audienceIntroducing 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

Current Approaches, Challenges, and Perspectives on Spatial OLAP for Agri-Environmental Analysis

International audienceSpatial OLAP (SOLAP) systems are powerful GeoBusiness Intelligence tools for analysing massive volumes of geo-referenced datasets. Therefore, these technologies are receiving considerable attention in the research community and in the database industry as well. Applications of these technologies are current in several domains such as ad marketing, healthcare, and urban development, to name a few. Contrary to other application domains, in the context of agri-environmental data and analysis, SOLAP systems have been underexploited. Therefore, in this paper, the author makes an exhaustive survey of most of the published studies in the domain of the SOLAP analysis of agri-environmental data with an emphasis on the reasons why only few recent works investigate the use of SOLAP systems in the agri-environmental context. In particular, the author focuses on the complexity of the spatio-multidimensional model and its implementation. Moreover, based on surveying the state of the art in this domain, this paper identifies some general guidelines that must be considered by the scientific community to design and implement efficient SOLAP approaches to the analysis of geo-referenced agri-environmental datasets. Finally, open issues about warehousing and OLAPing agri-environmental data are also shown in the paper