Transparent Provenance Derivation for User Decisions

Abstract

It is rare for data's history to include computational processes alone. Even when software generates data, users ultimately decide to execute software procedures, choose their configuration and inputs, reconfigure, halt and restart processes, and so on. Understanding the provenance of data thus involves understanding the reasoning of users behind these decisions, but demanding that users explicitly document decisions could be intrusive if implemented naively, and impractical in some cases. In this paper, therefore, we explore an approach to transparently deriving the provenance of user decisions at query time. The user reasoning is simulated, and if the result of the simulation matches the documented decision, the simulation is taken to approximate the actual reasoning. The plausibility of this approach requires that the simulation mirror human decision-making, so we adopt an automated process explicitly modelled on human psychology. The provenance of the decision is modelled in Open Provenance Model (OPM), allowing it to be queried as part of a larger provenance graph, and an OPM profile is provided to allow consistent querying of provenance across user decisions.

Improving privacy in health care with an ontology‐based provenance management system

“…Therefore, the author specifies that ontology‐based representations of provenance offer a uniform description of past executions and provide the reproducibility semantics for the OPM. An OPM‐based approach is proposed in Nunes, Chen, Miles, Luck, and Lucena () to derive the provenance of user decisions at the time of querying. The proposed approach is explicitly modelled on human psychology to simulate human decision, and the provenance of the decision is modelled in OPM.…”

“…The proposed approach is explicitly modelled on human psychology to simulate human decision, and the provenance of the decision is modelled in OPM. Also, an OPM profile is proposed to allow consistent querying of provenance across user decisions (Nunes et al, ).…”

A novel approach to provenance management for privacy preservation

“…In Moreau [23], it is specified that ontology-based representations of provenance offer a uniform description of past executions and provide the reproducibility semantics for the OPM. An OPM-based approach is proposed in Nunes et al [24] to derive the provenance of user decisions at the time of querying. The proposed approach is explicitly modelled on human psychology to simulate human decision and the provenance of the decision is modelled in OPM.…”

Provenance Research Issues and Challenges in the Big Data Era

“…Data provenance concerns with the problem of detecting the origin, the creation and the propagation process of data within a data-intensive system. In other words, data provenance consists in the lineage (e.g., [25]) and derivation (e.g., [21]) of data and data objects, and it puts its conceptual roots in extensively studies performed in the past in the contexts of arts, literary works, manuscripts, sculptures, and so forth. Another concept that is close to the "data provenance" one is represented by the so-called ownership of data (e.g., [20]), which refers to the issue of defining and providing information about the rightful owner of data assets, and to the acquisition, use and distribution policy implemented by the data owner.…”

“…For instance, one of the most successful data provenance techniques consists in the so-called annotationbased approaches (e.g., [21]) that propose modifying the input database queries in order to support data provenance tasks, while being able to access all the target data set. Obviously, the latter requirement becomes very hard when applied to big data repositories.…”