This book constitutes the proceedings of the 8th and 9th International Provenance and Annotation Workshop, IPAW 2020 and IPAW 2021 which were held as part of ProvenanceWeek in 2020 and 2021. Due to the COVID-19 pandemic, PropvenanceWeek 2020 was held as a 1-day virtual event with brief teaser talks on June 22, 2020. In 2021, the conference was held virtually during July 19-22, 2021. The 11 full papers and 12 posters and system demonstrations included in these proceedings were carefully reviewed and selected from a total of 31 submissions. They were organized in the following topical sections: provenance capture and representation; security; provenance types, inference, queries and summarization; reliability and trustworthiness; joint IPAW/TaPP poster and demonstration session.

This book constitutes the refereed proceedings of the 6th International Provenance and Annotation Workshop, IPAW 2016, held in McLean, VA, USA, in June 2016. The 12 revised full papers, 14 poster papers, and 2 demonstration papers presentedwere carefully reviewed and selected from 54 submissions. The papers feature state-of-the-art research and practice around the automatic capture, representation, and use of provenance. They are organized in topical sections on provenance capture, provenance analysis and visualization, and provenance models and applications.

The increasing complexity of systems and technologies in everyday use, make it hard or even impossible for humans to comprehend their function and behavior, and justify surprising observations. Explanation support can ease humans' interactions with technology: explanations can help users understand a system's function, justify system results, and increase their trust in automated decisions. In this book the authors provide an overview of existing work in explanation support for data-driven processes. In doing so, they classify explainability requirements across three dimensions: the target of the explanation ("What"), the audience of the explanation ("Who"), and the purpose of the explanation ("Why"). They identify dominant themes across these dimensions and the high-level desiderata each implies, accompanied by several examples to motivate various problem settings. Finally, they discuss explainability solutions through the lens of the "How" dimension: How something is explained (the form of the explanation) and how explanations are derived (methodology). This book provides researchers and system developers with a high-level overview of the complex problems encountered when developing better user interaction with modern large-scale data-driven computing systems and describes a roadmap to solving these issues in the future.

The term provenance is used in the art world to describe a record of the history of ownership of a piece of art. This term has been adapted by the database community to describe a record of the origin of a piece of data. Data provenance emerged as a research topic in the database community in the late 1990s. Data provenance, by explaining how the result of an operation was derived from its inputs, has proven to be a useful tool that is applicable in a wide variety of applications. This monograph gives a comprehensive introduction to data provenance concepts, algorithms, and methodology developed in the last few decades. It introduces the reader to the formalisms, algorithms, and system's developments in this fascinating field as well as providing a collection of relevant literature references for further research. The monograph provides a concise starting point for research into and using provenance in data. Although focusing on data provenance in databases pointers to work in other fields are given throughout. The intended audience is researchers and practitioners unfamiliar with the topic who want to develop a basic understanding of provenance techniques and the state-of-the-art in the field as well as researchers with prior experience in provenance that want to broaden their horizon.