Data warehouse means a decision support database allowing integration, organization, historisation, and management of data from heterogeneous sources, with the aim of exploiting them for decision-making. Data warehouses are essentially based on multidimensional model. This model organizes data into facts (subjects of analysis) and dimensions (axes of analysis). In classical data warehouses, facts are composed of numerical measures and dimensions which characterize it. Dimensions are organized

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... to hierarchical levels of detail. Based on the navigation and aggregation mechanisms offered by OLAP (On-Line Analytical Processing) tools, facts can be analyzed according to the desired level of detail. In real world applications, facts are not always numerical, and can be of qualitative nature. In addition, sometimes a human expert or learned model such as a decision tree provides a qualitative evaluation of phenomenon based on its different parameters i.e. dimensions. Conventional data warehouses are thus not adapted to qualitative reasoning and have not the ability to deal with qualitative data. In previous work, we have proposed an original approach of qualitative data warehouse modeling, which permits integrating qualitative measures. Based on computing with words methodology, we have extended classical multidimensional data model to allow the aggregation and analysis of qualitative data in OLAP environment. We have implemented this model in a Spatial Decision Support System to help managers of public spaces to reduce annoyances and improve the quality of life of the citizens. In this paper, we will focus our study on the representation and management of imprecision in annoyance analysis process. The main objective of this process consists in determining the least harmful scenario of urban building sites, particularly in dense urban environments.