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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.
This book covers important aspects of fundamental research in data provenance and data management(DPDM), including provenance representation and querying, as well as practical applications in such domains as clinical trials, bioinformatics and radio astronomy.
This book constitutes the thoroughly refereed post-conference proceedings of the Second International Provenance and Annotation Workshop, IPAW 2008, held in Salt Lake City, UT, USA, in June 2007. The 14 revised full papers and 15 revised short and demo papers presented together with 2 keynote lectures were carefully reviewed and selected from 40 submissions. The paper are organized in topical sections on provenance: models and querying; provenance: visualization, failures, identity; provenance and workflows; provenance for streams and collaboration; and applications.
This book constitutes the revised selected papers of the 5th International Provenance and Annotation Workshop, IPAW 2014, held in Cologne, Germany in June 2014. The 14 long papers, 20 short papers and 4 extended abstracts presented were carefully reviewed and selected from 53 submissions. The papers include tools that enable provenance capture from software compilers, from web publications and from scripts, using existing audit logs and employing both static and dynamic instrumentation.
This book constitutes the thoroughly referred post-proceedings of the International Provenance and Annotation Workshops, IPAW 2006, held in Chicago, Il, USA in May 2006. The 26 revised full papers presented together with two keynote papers were carefully selected for presentation during two rounds of reviewing and improvement. The papers are organized in topical sections.
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. - Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand - Enables you to build your own algorithms and implement your own data integration applications
With an ever-increasing amount of information on the web, it is critical to understand the pedigree, quality, and accuracy of your data. Using provenance, you can ascertain the quality of data based on its ancestral data and derivations, track back to sources of errors, allow automatic re-enactment of derivations to update data, and provide attribution of the data source. Secure Data Provenance and Inference Control with Semantic Web supplies step-by-step instructions on how to secure the provenance of your data to make sure it is safe from inference attacks. It details the design and implementation of a policy engine for provenance of data and presents case studies that illustrate solutions in a typical distributed health care system for hospitals. Although the case studies describe solutions in the health care domain, you can easily apply the methods presented in the book to a range of other domains. The book describes the design and implementation of a policy engine for provenance and demonstrates the use of Semantic Web technologies and cloud computing technologies to enhance the scalability of solutions. It covers Semantic Web technologies for the representation and reasoning of the provenance of the data and provides a unifying framework for securing provenance that can help to address the various criteria of your information systems. Illustrating key concepts and practical techniques, the book considers cloud computing technologies that can enhance the scalability of solutions. After reading this book you will be better prepared to keep up with the on-going development of the prototypes, products, tools, and standards for secure data management, secure Semantic Web, secure web services, and secure cloud computing.
Reviews research over the past ten years on why, how, and where provenance, clarifies the relationships among these notions of provenance, and describes some of their applications in confidence computation, view maintenance and update, debugging, and annotation propagation
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.