Issues and Challenges of KDD Model for Distributed Data Mining Techniques and Architecture

We are currently seeing a data explosion and witnessing a meteoric rise in database sizes; therefore, with the widespread availability of such large volumes of data, there has arisen an urgent need to transform it into actionable insights and knowledge. So, data mining is a very important tool. The knowledge that is produced is put to use in a wide variety of contexts, containing but not limited to the following: science, technology, engineering, commerce, study, social, health, education, entertainment, etc. Market analysis, client retention, and production control are just some of the many uses for the knowledge gained from data. In this study, we explore the systems, methodologies, techniques, and algorithms for Distributed Data Mining that may be used to effectively and efficiently deal with distributed data and extract useful insights. Moreover, some selected KDD models for Distributed Data Mining have been systematically reviewed.