Fuzzy OLAP cube for qualitative analysis

Data warehouse systems enable decision-makers to acquire and integrate information from heterogeneous sources and query very large databases efficiently. Data warehouse provides the users with an online analytical processing (OLAP) facility, by which they can query the data warehouse. OLAP cubes Provide aggregate inforniation to support the analysis of the contents of data warehouse. Usually the relationship between the data and the queries on data are fuzzy in nature. For example, the query that gives whether the sale of a costly product is Good, Average, or poor is very useful for decision makers. The data warehouse must be designed to address such business requirements in order to allow intelligent decision-making. In this paper, we introduce a fizzy OLAP cube to support qualitative analysis for data warehousing. We also present various OLAP operations that, can be performed on the fuzzy data cube using the membership values. These values are derived through the cluster based membership functions by considering multiattribute summarization. To illustrate our approach, we have developed an application prototype in a Sales data-warehousing domain.