The chapter "Visual Analytics: Definition, Process, and Challenges" by Daniel Keim, Gennady Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melançon provides a comprehensive overview of visual analytics, a field that aims to address the challenges of information overload by combining automated analysis techniques with interactive visualizations. The authors define visual analytics as the science of analytical reasoning facilitated by interactive visual interfaces, emphasizing its role in turning raw data into actionable knowledge. They highlight the importance of visual analytics in various domains such as engineering, financial analysis, socio-economic studies, public safety, and environmental monitoring. The chapter discusses the process of visual analytics, which involves a sense-making loop where users interact with visual representations to gain insights and refine their understanding. It also addresses the technical challenges, including scalability, data quality, visual representation, user interfaces, and evaluation methods. The authors conclude by emphasizing the need for a collaborative and interdisciplinary approach to develop effective visual analytics solutions, leveraging the strengths of both human and machine capabilities.The chapter "Visual Analytics: Definition, Process, and Challenges" by Daniel Keim, Gennady Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melançon provides a comprehensive overview of visual analytics, a field that aims to address the challenges of information overload by combining automated analysis techniques with interactive visualizations. The authors define visual analytics as the science of analytical reasoning facilitated by interactive visual interfaces, emphasizing its role in turning raw data into actionable knowledge. They highlight the importance of visual analytics in various domains such as engineering, financial analysis, socio-economic studies, public safety, and environmental monitoring. The chapter discusses the process of visual analytics, which involves a sense-making loop where users interact with visual representations to gain insights and refine their understanding. It also addresses the technical challenges, including scalability, data quality, visual representation, user interfaces, and evaluation methods. The authors conclude by emphasizing the need for a collaborative and interdisciplinary approach to develop effective visual analytics solutions, leveraging the strengths of both human and machine capabilities.