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Versioned-PROV: A PROV extension to support mutable data entities

Lookup NU author(s): Professor Paolo MissierORCiD


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© Springer Nature Switzerland AG 2018. The PROV data model assumes that entities are immutable and all changes to an entity e are represented by the creation of a new entity e’. This is reasonable for many provenance applications but may produce verbose results once we move towards fine-grained provenance due to the possibility of multiple binds (i.e., variables, elements of data structures) referring to the same mutable data objects (e.g., lists or dictionaries in Python). Changing a data object that is referenced by multiple immutable entities requires duplicating those immutable entities to keep consistency. This imposes an overhead on the provenance storage and makes it hard to represent data-changing operations and their effect on the provenance graph. In this paper, we propose a PROV extension to represent mutable data structures. We do this by adding reference derivations and checkpoints. We evaluate our approach by comparing it to plain PROV and PROV-Dictionary. Results indicate a reduction in the storage overhead for assignments and changes in data structures from O(N) and Ώ(R × N), respectively, to O(1) in both cases when compared to plain PROV (N is the number of members in the data structure and R is the number of references to the data structure).

Publication metadata

Author(s): Pimentel JFN, Missier P, Murta L, Braganholo V

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 7th International Provenance and Annotation Workshop, IPAW 2018

Year of Conference: 2018

Pages: 87-100

Online publication date: 06/09/2018

Acceptance date: 09/07/2018

Publisher: Springer Verlag


DOI: 10.1007/978-3-319-98379-0_7

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN: 9783319983783