A Model for Fine-Grained Data Citation

An increasing amount of information is being collected in structured, evolving, curated databases, driving the question of how information extracted from such datasets via queries should be cited. Unlike traditional research products, such books and journals, which have a fixed granularity, data citation is a challenge because the granularity varies. Different portions of the database, with varying granularity, may have different citations. Furthermore, there are an infinite number of queries over a database, each accessing and generating different subsets of the database, so we cannot hope to explicitly attach a citation to every possible result set and/or query. We present the novel problem of automatically generating citations for general queries over a relational database, and explore a solution based on a set of citation views, each of which attaches a citation to a view of the database. Citation views are then used to automatically construct citations for general queries. Our approach draws inspiration from results in two areas, query rewriting using views and database provenance and combines them in a robust model. We then discuss open issues in developing a practical solution to this challenging problem.