Provenance refers to information about the source, origin,
derivation or authorship of data. It has deep connections to
topics such as program slicing, causality, explanation, and
information flow security. The Principles of Provenance
group performs fundamental research on provenance as well as
principled system development.
- Skye: A programming language bridging theory and practice
for scientific data curation, funded by an ERC Consolidator
- A Diagnostics Approach to Advanced Persistent Threat
Prevention (ADAPT), in collaboration with Galois, Inc., Xerox
PARC, and Oregon State University, funded by DARPA's
Transparent Computing research program
- Language-integrated provenance, funded by a Google Research Award
- Provenance for configuration language security (Microsoft
Research), in collaboration with Paul Anderson (Edinburgh) and Dimitrios
- Language-based provenance security (AFOSR EOARD)
For a more complete listing of provenance-related publications by
us and other researchers in Edinburgh, see this page.
- Language-integrated provenance, Stefan Fehrenbach
and James Cheney, PPDP 2016, p. 214-227. (arXiv)
- Causally consistent dynamic slicing, Roly Perera, Deepak Garg and James Cheney, CONCUR
2016, p. 18:1-18:15.
- Provenance segmentation, Rui Abreu, Dave Archer, Erin
Chapman, Hoda Eldardiry, James Cheney, and Adria Gascon, TaPP 2016.
- The Rationale of PROV, Luc Moreau, Paul Groth,
James Cheney, Simon Miles and Timothy Lebo, Journal of Web
Semantics, 35:235-257, 2015.
- Proof-relevant π-calculus, Roly Perera and James
Cheney, LFMTP 2015, p. 46-70.
- Language-integrated provenance in Links, Stefan Fehrenbach
and James Cheney, TaPP 2015.
- Dynamic provenance for SPARQL Updates, Harry Halpin
and James Cheney, ISWC 2014, p. 425-440 (arxiv)
- Database Queries that Explain their Work, James
Cheney, Amal Ahmed and Umut Acar. PPDP 2014, p. 271-282 (arXiv)