CIS700: Advanced Topics in Databases: Data Provenance and Data Citation
Susan B. Davidson: 566 Levine North, 898-3490, firstname.lastname@example.org
Prerequisites: CIS 550 or equivalent
Textbook: Research papers will be made available over the web, linked to the course syllabus.
Time and Location: MW 1:30-3, Levine 512
In today’s Big Data-driven science, there is a well-acknowledged need for reproducibility, repeatability, and consistent processing of data. Such capabilities require data provenance, a comprehensive record of the inputs, setting, and processing operations that went into producing a result. The course will cover the theory and practice of data provenance: How it is represented and captured (database, workflow, OS-level, network); Connections to graph databases and query languages (e.g. NoSQL solutions such as REDIS, ProQL); Privacy and security issues; Provenance interoperability, the WWW standard PROV and limitations; Partial provenance; and other current research questions.
Closely related to provenance is the issue of data citation. Citation is an essential part of scientific publishing, is used to gauge the trust placed in published information and, for better or for worse, is an important factor in judging academic reputation. Scientific publishing increasingly involves datasets placed in structured yet evolving databases, and accessed through queries. Although standards have been proposed for citing such datasets, it is not well understood how to automatically generate citations. The course will cover the practice of and computational challenges associated with data citation: Exemplars of citeable datasets; Rule-based citation language; Data archiving; Relationship to query rewriting using views; and other related research questions.