Automating data citation: the eagle-i experience.

Data citation is of growing concern for owners of curated databases, who wish to give credit to the contributors and curators responsible for portions of the dataset and enable the data retrieved by a query to be later examined. While several databases specify how data should be cited, they leave it to users to manually construct the citations and do not generate them automatically. We report our experiences in automating data citation for an RDF dataset called eagle-i, and discuss how to generalize this to a citation framework that can work across a variety of different types of databases (e.

Data Citation: a Computational Challenge.

Data citation is an interesting computational challenge, whose solution draws on several well-studied problems in database theory: query answering using views, and provenance. We describe the problem, suggest an approach to its solution, and highlight several open research problems, both practical and theoretical.

PROX: Approximated Summarization of Data Provenance.

Many modern applications involve collecting large amounts of data from multiple sources, and then aggregating and manipulating it in intricate ways. The complexity of such applications, combined with the size of the collected data, makes it difficult to understand the application logic and how information was derived. has been proven helpful in this respect in different contexts; however, maintaining and presenting the full and exact provenance may be infeasible, due to its size and complex structure.