Investigation and development of fuzzy logic based analytics for data warehousing

Abstract

Data warehouse is a widely used technology that provides the employees who take strategic decisions within an enterprise with access to any level of required data. Historically, data warehouses were built on crisp values with a key assumption that one attribute value falls into one nominal value. Fuzzy Logic can be built into the data warehouse by treating the dimension value as weightages of different labels. However, in most of the attempts to implement a fuzzy data warehouse, they were limited and non-comprehensive in the implementation when considering end to end aspects of the data warehouse. Using fuzzy techniques, it is possible to represent fuzzy conceptual information in the original domain, that would lead to better analysis. In this research, different types of fuzzy membership functions are defined using different techniques and data warehouse facts and dimensions are designed accordingly. There can be multiple fuzzy functions for one dimension as well as for one fact table depending on the business domain. Apart from defining fuzzy membership function using data-driven methods, there are other approaches of defining fuzzy membership functions such as a derived method where multiple fuzzy memberships are combined to define several fuzzy membership functions. In the literature reviewed, concepts like ETL and OLAP cube were found to be discussed in a limited manner. Non-function techniques are also identified and addressed in the means of validation, configuration, performance, security, scalability in order to make better usability of the fuzzy data warehouse. The scope of this research revolves around end-to-end features of fuzzy data warehousing starting from data extraction and transformation to data warehouse modeling. Implementing a fuzzy data warehouse, helps to enable users with better analyses. To verify whether the proposed fuzzy data warehouse can be applied, a feasibility study is carried out for the domains in which fuzzy data warehousing can be implemented. Concepts related to the outcome from this research are verified with the use of a Sri Lankan plantation data set for four years. The results show that concepts introduced by this research can be implemented in realistic scenarios.