Name
Abstract | ||
|---|---|---|
Although many real world phenomena are vague and characterized by having uncertain location or vague shape, existing spatial data warehouse models do not support spatial vagueness and then cannot properly represent these phenomena. In this paper, we propose the VSCube conceptual model to represent and manipulate shape vagueness in spatial data warehouses, allowing the analysis of business scores related to vague spatial data, and therefore improving the decision-making process. Our VSCube conceptual model is based on the cube metaphor and supports geometric shapes and the corresponding membership values, thus providing more expressiveness to represent vague spatial data. We also define vague spatial aggregation functions (e.g. vague spatial union) and vague spatial predicates to enable vague SOLAP queries (e.g. intersection range queries). Finally, we introduce the concept of vague SOLAP and its operations (e.g. drill-down and roll-up). We demonstrate the applicability of our model by describing an application concerning pest control in agriculture and by discussing the reuse of existing models in the VSCube conceptual model. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10707-013-0186-y | GeoInformatica |
Keywords | Field | DocType |
Spatial data warehouse,Conceptual modeling,Spatial vagueness,Vague spatial object | Spatial analysis,Data mining,Vagueness,Conceptual model,Reuse,Range query (data structures),Geometric shape,Predicate (grammar),Metaphor,Mathematics | Journal |
Volume | Issue | ISSN |
18 | 2 | 1384-6175 |
Citations | PageRank | References |
5 | 0.53 | 24 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thiago Luís Siqueira | 1 | 5 | 0.53 |
| Cristina Dutra De Aguiar Ciferri | 2 | 166 | 27.92 |
| Valéria Cesário Times | 3 | 182 | 27.52 |
| Ricardo Rodrigues Ciferri | 4 | 137 | 22.24 |
| SiqueiraThiago Luís | 5 | 5 | 0.53 |
| CiferriCristina Dutra De | 6 | 5 | 0.53 |
| TimesValéria Cesário | 7 | 6 | 0.91 |
| CiferriRicardo Rodrigues | 8 | 7 | 1.60 |