A systematic review of provenance systems

Pérez, Beatriz; Rubio, Julio; Sáenz-Adán, Carlos

doi:10.1007/s10115-018-1164-3

Cited by 50 publications

(38 citation statements)

References 80 publications

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“…The field of data provenance can provide these principles. Pérez defined provenance to be "all the information and relationships that contributed to the existence of a piece Figure 1: W3C PROV provenance graph structure of data" [31]. Within that field, the Open Provenance Model (OPM) provides a standard reusable ontology to record provenance information [29].…”

Section: Provenancementioning

confidence: 99%

“…By logging the changes to the model, rather than the changes to the system, we can abstract away details and therefore reduce the volume of the logs. Knowing the provenance of the changes [31], (i.e. who made those changes and for what reasons), creates the needed causal connections that can help answer questions about the system's behaviour.…”

Section: Introductionmentioning

confidence: 99%

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Towards automated provenance collection for runtime models to record system history

Reynolds

García-Domínguez

Bencomo

2020

Proceedings of the 12th System Analysis and Modelling Conference

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In highly dynamic environments, systems are expected to make decisions on the fly based on their observations that are bound to be partial. As such, the reasons for its runtime behaviour may be difficult to understand. In these cases, accountability is crucial, and decisions by the system need to be traceable. Logging is essential to support explanations of behaviour, but it poses challenges. Concerns about analysing massive logs have motivated the introduction of structured logging, however, knowing what to log and which details to include is still a challenge. Structured logs still do not necessarily relate events to each other, or indicate time intervals. We argue that logging changes to a runtime model in a provenance graph can mitigate some of these problems. The runtime model keeps only relevant details, therefore reducing the volume of the logs, while the provenance graph records causal connections between the changes and the activities performed by the agents in the system that have introduced them. In this paper, we demonstrate a first version towards a reusable infrastructure for the automated construction of such a provenance graph. We apply it to a multithreaded traffic simulation case study, with multiple concurrent agents managing different parts of the simulation. We show how the provenance graphs can support validating the system behaviour, and how a seeded fault is reflected in the provenance graphs. CCS CONCEPTS • Software and its engineering → System modeling languages; Integration frameworks; Model-driven software engineering.

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Section: Provenancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards automated provenance collection for runtime models to record system history

Reynolds

García-Domínguez

Bencomo

2020

Proceedings of the 12th System Analysis and Modelling Conference

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“…This is a problem studied within the field of data provenance. In their systematic review [24], Pérez defines provenance to be "all the information and relationships that contributed to the existence of a piece of data". Provenance was first related to works of art, but the concepts have been applied to other use cases such as scientific experiments [19].…”

Section: Tracking Why Models Change Using Provenancementioning

confidence: 99%

Automated provenance graphs for models@run.time

Reynolds

García-Domínguez

Bencomo

2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Software systems are increasingly making decisions autonomously by incorporating AI and machine learning capabilities. These systems are known as self-adaptive and autonomous systems (SAS). Some of these decisions can have a life-changing impact on the people involved and therefore, they need to be appropriately tracked and justified: the system should not be taken as a black box. It is required to be able to have knowledge about past events and records of history of the decision making. However, tracking everything that was going on in the system at the time a decision was made may be unfeasible, due to resource constraints and complexity. In this paper, we propose an approach that combines the abstraction and reasoning support offered by models used at runtime with provenance graphs that capture the key decisions made by a system through its execution. Provenance graphs relate the entities, actors and activities that take place in the system over time, allowing for tracing the reasons why the system reached its current state. We introduce activity scopes, which highlight the high-level activities taking place for each decision, and reduce the cost of instrumenting a system to automatically produce provenance graphs of these decisions. We demonstrate a proof of concept implementation of our proposal across two case studies, and present a roadmap towards a reusable provenance layer based on the experiments. CCS CONCEPTS • Software and its engineering → Abstraction, modeling and modularity; • Computing methodologies → Model development and analysis; • Applied computing → Evidence collection, storage and analysis.

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“…Provenance tracking, storage, and retrieval has also been an active research topic in the database community. Recent surveys by Herschel et al [16] and Pérez et al [28] summarize the state-of-the-art. However, most of the works focus on efficient algorithms to store and retrieve information, while the visualization of and interaction with the search results play a tangential role.…”

Section: Index and Provenancementioning

confidence: 99%

KnowledgePearls: Provenance-Based Visualization Retrieval

Stitz

Gratzl

Piringer

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.

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A systematic review of provenance systems

Cited by 50 publications

References 80 publications

Towards automated provenance collection for runtime models to record system history

Towards automated provenance collection for runtime models to record system history

Automated provenance graphs for models@run.time

KnowledgePearls: Provenance-Based Visualization Retrieval

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