Intelligent Decision Making Based on Data Mining Using Differential Evolution Algorithms and Framework for ETL Workflow Management

Abstract

Business decision-making is not a simple task. There are many reasons for that but one of the main reason might be that data comes from different operational sources of an organization and it is difficult to organize and maintain especially if a huge volume of data is involved. A Data warehouse is helpful in this regard as it can assist in business decision-making by collecting data from different operational sources of the organization. To collect the data from different sources and then loading it into a warehouse is also difficult to manage the data on these sources is not in a consistent format. It is usually a three step process that involves extraction, transformation and loading. The extraction of data from these different sources, then transform it into a consistent format and consequently load it into the warehouse, by using ETL (Extract, transform and load) tools. Nowadays, the majority of ETL tools organize workflow. An ETL workflow can be considered as a group of ETL jobs with dependencies between them. In this research different considerations are discussed which are necessary for the proper management of workflow in context of efficient business decision-making process. The purpose of this research paper is to give a novel idea about the possibility of designing an intelligent decision support system by using the techniques of data mining as well as the differential evolution algorithm of artificial neural networks. Artificial neural networks are being used to enhance the capabilities of the intelligent decision support system. A preexisting differential evolution algorithm with slight modification is being used within the DSS environment. It has been assumed that this merger will lead towards more development and advancement within the concept of DSS.