A New Approach of Imprecision Management in Qualitative Data Warehouse

• Published in: Uncertainty Modelling and Quality Control for Spatial Data pp. 115 - 132
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2016; Taylor & Francis Group
• Subjects: ENVIRONMENTAL ENGINEERING & TECHNOLOGY; Geochemistry; Human geography; Β1 Γ1 Γ1 Γ1 Γ1; Words Methodology; Microsoft SQL Server; SPC; Annoyance Notion; Data Warehouse; Qualitative Fashion; Urban Building Site; Fuzzy Set; Fuzzy Inference; User Defined Functions; Multidimensional Data Model; OLAP Tool; Membership Function; Annoyance Evaluation; Linguistic Degrees; DOP; OLAP; Fuzzy Relation; Multidimensional Model; OLAP Query; Human Expert; OLAP Server; ETL; Α2 Α3 Α4 Α5 Β1
• ISBN: 9781498733281; 149873328X
• DOI: 10.1201/b19160-13

ABSTRACT Data warehouse means a decision support database allowing integration, organization, historization, and management of data from heterogeneous sources, with the aim of exploiting them for decision making. Data warehouses are essentially based on a 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 that characterize them. Dimensions are organized into hierarchical levels of detail. Based on the navigation and aggregation mechanisms offered by online analytical processing (OLAP) tools, facts can be analyzed according to the desired level of detail. In real-world applications,CONTENTS7.1 Introduction ... 102 7.2 Motivation and Use Case: Urban Building Site Annoyance ... 1037.2.1 Qualitative Representation of Annoyance ... 103 7.2.1.1 Notion of Annoyance ... 103 7.2.1.2 Dimensions of Annoyance ... 1047.2.2 Annoyance Evaluation ... 106 7.3 Toward a Qualitative Multidimensional Model for HandlingImprecise Data... 107 7.3.1 Multidimensional Data Model of Annoyance ... 107 7.3.2 Fuzzy Data Model ... 1097.3.2.1 Application ... 109 7.3.2.2 Fuzzy Fusion ... 1117.4 Prototype and Experimentation ... 115 7.4.1 Implementation of Annoyance Data Warehouse ... 116 7.4.2 Implementation of Fuzzy Fusion in Data Warehouse ... 1167.5 Conclusion and Future Work ... 116 References ... 117facts are not always numerical and can be of a qualitative nature. In addition, sometimes a human expert or learned model such as a decision tree provides a qualitative evaluation of the phenomenon based on its different parameters, that is, dimensions. Conventional data warehouses are thus not adapted to qualitative reasoning and do not have the ability to deal with qualitative data. In previous work, we have proposed an original approach for qualitative data warehouse modelling that permits integrating qualitative measures. Based on computing with words methodology, we have extended the classical multidimensional data model to allow the aggregation and analysis of qualitative data in the OLAP environment. In this chapter, we focus our study on the representation and management of imprecision in the annoyance analysis process.