Xu, KaiOttley, AlvittaWalchshofer, ConnyStreit, MarcChang, RemcoWenskovitch, JohnSmit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei2020-05-242020-05-2420201467-8659https://doi.org/10.1111/cgf.14035https://diglib.eg.org:443/handle/10.1111/cgf14035There is fast-growing literature on provenance-related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence-based guidelines and highlights concrete opportunities for future development in this emerging area.Attribution 4.0 International License10.1111/cgf.14035757-783