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An UML Profile and SOLAP Datacubes Multidimensional Schemas Transformation Process for Datacubes Risk-Aware Design
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Author(s): Elodie Edoh-Alove (National Research Institute for Science and Technology for the Environment and Agriculture (IRSTEA), Aubiere, France), Sandro Bimonte (National Research Institute for Science and Technology for the Environment and Agriculture (IRSTEA), Aubiere, France)and François Pinet (National Research Institute for Science and Technology for the Environment and Agriculture (IRSTEA), Aubiere, France)
Copyright: 2015
Volume: 11
Issue: 4
Pages: 20
Source title:
International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/ijdwm.2015100104
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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.
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