A Provenance Tracking Model for Data Updates

TL;DR

This paper introduces a formal calculus for tracking data provenance during updates in decentralized systems, providing a semantics that accounts for concurrency and data quality assessment.

Contribution

It presents a novel calculus and semantics for data updates and provenance in concurrent, decentralized systems, extending existing provenance models.

Findings

Provides a formal semantics for data updates and provenance in concurrent systems

Develops a model based on series-parallel DAGs for provenance tracking

Enhances understanding of data quality in decentralized data systems

Abstract

For data-centric systems, provenance tracking is particularly important when the system is open and decentralised, such as the Web of Linked Data. In this paper, a concise but expressive calculus which models data updates is presented. The calculus is used to provide an operational semantics for a system where data and updates interact concurrently. The operational semantics of the calculus also tracks the provenance of data with respect to updates. This provides a new formal semantics extending provenance diagrams which takes into account the execution of processes in a concurrent setting. Moreover, a sound and complete model for the calculus based on ideals of series-parallel DAGs is provided. The notion of provenance introduced can be used as a subjective indicator of the quality of data in concurrent interacting systems.