An UML Profile and SOLAP Datacubes Multidimensional Schemas Transformation Process for Datacubes Risk-Aware Design

Abstract

Spatial Data Warehouses (SDWs) and Spatial On-Line Analytical Processing (SOLAP) systems are new technologies for the integration and the analysis of huge volume of data with spatial reference. Spatial vagueness is often neglected in these types of systems and the data and analysis results are considered reliable. In a previous work, the authors provided a new design method for SOLAP datacubes that allows the handling of vague spatial data analysis issues. The method consists of tailoring SOLAP datacubes schemas to end-users tolerance levels to identified potential risks of misinterpretation they encounter when exploiting datacubes containing vague spatial data. It this paper, the authors further their previous proposal by presenting different formal tools to support their method: it is an UML profile providing stereotypes needed to add vague, risks and tolerance levels information on datacubes schemas plus the formal definition of SOLAP datacubes schemas transformation process and functions.

A foundation for spatial data warehouses on the Semantic Web

“…We denote a QB4SOLAP graph by G, where G ⊂ T .The cube schema and cube instances are subsets of this graph and are denoted, respectively, by G S and G I , where G S ⊂ G and G I ⊂ G. Given an MD element x ∈ (I ∪ B) in a schema graph or instance graph G, we define by G x the subgraph of G for x, where G x ⊂ G. We define the function id(x) : G → I, that given a MD element x returns its identifier I from the graph G. We use superscript notation to indicate the type of the identifier from the cube schema graph (G S ) and cube instance graph (G I ), e.g., id S (x) for a cube schema identifier and id I (x) for a cube instance identifier. 9 RDF cube: http://purl.org/linked-data/cube# 10 QB4OLAP: http://purl.org/qb4olap/cubes# 11 QB4SOLAP: http://w3id.org/qb4solap# 12 GeoSPARQL:http://www.opengis.net/ont/ geosparql# An n-dimensional cube schema CS is a tuple CS = (D, M, F ), with a set of dimensions D, a set of measures M , and a fact…”

“…Bédard et al first introduced the term SOLAP [4] in 1997. SOLAP systems [9,33] since then, have significantly been improved. Respectively, various papers improve the spatial aggregation functions and techniques [6,14,28,38].…”