Knowledge Discovery Frameworks and Characteristics

Abstract

Knowledge is an abstract concept with no tangible connection to the physical world. Knowledge discovery refers to extracting knowledge from data and emphasizes the high-level application of specific techniques. The purpose of the knowledge discovery process is to extract knowledge from data. Knowledge discovery frameworks are a structured approach that combines various techniques and tools from different fields, such as data mining, machine learning, statistics, information visualization, and knowledge discovery process models, and includes underlying technologies to assist in the knowledge discovery systems development process. The primary goal of knowledge discovery systems is to identify patterns and relationships in the data that can be used to gain new insights and improve decision-making by applying a combination of tools, technologies, and techniques. The complexity of knowledge discovery systems can proliferate, making the use of knowledge discovery frameworks, design patterns, and process models important. This research summarizes the characteristics of knowledge discovery frameworks available for developing flexible and scalable knowledge discovery systems. The purpose is to determine if the characteristics indicate that the existing frameworks can support flexible and scalable knowledge discovery systems development according to modern design principles. In order to identify the knowledge discovery framework characteristics, authors apply structured literature research and identify the underlying knowledge discovery process models and characteristics of knowledge discovery frameworks.