Models for Knowledge Extraction from Huge Datasets

Abstract

Abstract: This article provides a comparative analysis of models for discovering and extracting knowledge from huge data sets. Different methods for exploring and identifying hidden or complex relationships among data are presented. The purpose is to discover useful and understandable knowledge. The end result is making informed and optimal decisions. The implementation of an additional module called Intelligent Knowledge Broker complements and enriches the concept of Knowledge Discovery from huge Data Bases (KDDB). Its task is to provide the required knowledge upon inquiry and to have a connection to external systems in order to synchronize and update the studied area with new data and knowledge. The concept of knowledge extraction from huge datasets enriched with new technological advances of AI is an exciting multidisciplinary area of research that has useful applications.