Methodologies for Knowledge Discovery Processes in Context of AstroGeoInformatics

Abstract Successful data science projects usually follow some methodology which can provide the data scientist with basic guidelines on how to challenge the problem and how to work with data, algorithms, or models. This methodology is then a structured way to describe the knowledge discovery process. Without a flexible structure of steps, data science projects can be unsuccessful, or at least it will be hard to achieve a result that can be easily applied and shared. Their better understanding is quite beneficial both to data scientists and to anyone who needs to discuss results or steps of the process. Moreover, in some domains, including those working with data from astronomy and geophysics, steps used in preprocessing and analysis of data are crucial to understanding provided data products. In this chapter, we provide an overview of knowledge discovery processes, selected methodologies, and their standardization and sharing using process languages and ontologies. At the end of the chapter, we also discuss these aspects according to the domain of astro/geo data.