This paper proposes a novel perspective of research on the challenging issue of modeling Spatial Data Warehouses (SDW) that nicely contributes to improve state-of-the-art proposals. This conveys in the so-called Spatial Data Warehouse Metamodel (SDWM) that allow us to enhance both coverage and expressive power of SDW modeling by means of the following amenities: (i) separating the conceptual SDW modeling from the conceptual (spatial) OLAP modeling; (ii) supporting the modeling of complex constructs in SDW; and (iii) stereotyping attributes and measures as spatial objects directly. All these contributions finally depict a novel perspective of research in the investigated scientific field, which breaks the actual trend of state-of-the-art initiatives, by pinpointing their limitations. We complete our analytical contribution by means of a real-life application implemented via SDWM, which highlights the benefits deriving from applying SDWM in contrast with traditional SDW modeling methodologies. © 2012 Springer-Verlag.
Enhancing coverage and expressive power of spatial data warehousing modeling: The SDWM approach
Cuzzocrea Alfredo;
2012
Abstract
This paper proposes a novel perspective of research on the challenging issue of modeling Spatial Data Warehouses (SDW) that nicely contributes to improve state-of-the-art proposals. This conveys in the so-called Spatial Data Warehouse Metamodel (SDWM) that allow us to enhance both coverage and expressive power of SDW modeling by means of the following amenities: (i) separating the conceptual SDW modeling from the conceptual (spatial) OLAP modeling; (ii) supporting the modeling of complex constructs in SDW; and (iii) stereotyping attributes and measures as spatial objects directly. All these contributions finally depict a novel perspective of research in the investigated scientific field, which breaks the actual trend of state-of-the-art initiatives, by pinpointing their limitations. We complete our analytical contribution by means of a real-life application implemented via SDWM, which highlights the benefits deriving from applying SDWM in contrast with traditional SDW modeling methodologies. © 2012 Springer-Verlag.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


