Building fuzzy OLAP using multi-attribute summarization

Abstract

Data Warehouse helps the decision makers of an organization in taking decisions that helps in improving the profitability of business by consolidating and aggregating data from many heterogeneous sources. Information available in this aggregated data is raw numbers. These raw numbers does not provide semantics about the data to decision makers. For example, " A sale of amount 100000 is good or bad is unclear". Usually the relationship between the data and requirements to the decision maker are fuzzy in nature, rather than crisp numbers. There is a need to design data-warehouse in such a way that it should address the requirements of intelligent decision-making. In this paper, we build a fuzzy OLAP cube to support qualitative data analysis by using multi-attribute summarization. Data is fuzzified and assigned membership values using a cluster-based approach. To demonstrate the model, we developed a prototype data warehouse for foreign exchange currency transactions and analyzed these transactions with fuzzy OLAP operations. © 2007 IEEE.