Exploiting Provenance to Make Sense of Automated Decisions in Scientific Workflows

Missier, Paolo; Embury, Suzanne M.; Stapenhurst, Richard

doi:10.1007/978-3-540-89965-5_19

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…There are many systems that collect provenance and several excellent survey papers on provenance systems (Freire et al, 2008;Herschel et al, 2018;Pimentel et al, 2019). Provenance collection is common in workflow systems where it is built directly into the execution environment, such as in Kepler (Altintas et al, 2006), VisTrails (Koop et al, 2013), and Taverna (Missier et al, 2008). Of particular interest is the work of de Oliveira et al (2014) who use provenance to debug long-running workflows, and Why-Diff (Thavasimani et al, 2019) which compares provenance of multiple workflow executions to find differences.…”

Section: Related Workmentioning

confidence: 99%

Making Provenance Work for You

et al. 2023

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To be useful, scientific results must be reproducible and trustworthy. Data provenance-the history of data and how it was computed-underlies reproducibility of, and trust in, data analyses. Our work focuses on collecting data provenance from R scripts and providing tools that use the provenance to increase the reproducibility of and trust in analyses done in R. Specifically, our "End-toend provenance tools" ("E2ETools") use data provenance to: document the computing environment and inputs and outputs of a script's execution; support script debugging and exploration; and explain differences in behavior across repeated executions of the same script. Use of these tools can help both the original author and later users of a script reproduce and trust its results.

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Section: Related Workmentioning

confidence: 99%

Making Provenance Work for You

et al. 2023

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“…Other work concerns the provenance of decisions, but again concern the gathering of data to inform the decision rather than the decision itself. For example, Kifor et al [5] investigate the provenance of organ transplant decisions, but the decision itself is not modelled, only the observable factors used as input, while Missier et al [8] record the quality of inputs to an automated decision, based on user criteria, to interpret the trustworthiness of the result. In the following sections, we first define the problem and provide a motivating example, before presenting the overall approach and its components: an automated decision maker and an OPM profile for user decisions, to later detail questions that can be answered regarding the human decisions.…”

Section: Introductionmentioning

confidence: 99%

Transparent Provenance Derivation for User Decisions

Nunes

Chen

Miles

et al. 2012

Lecture Notes in Computer Science

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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.

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The Foundations for Provenance on the Web

Moreau¹

2010

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Provenance, i.e., the origin or source of something, is becoming an important concern, since it offers the means to verify data products, to infer their quality, to analyse the processes that led to them, and to decide whether they can be trusted. For instance, provenance enables the reproducibility of scientific results; provenance is necessary to track attribution and credit in curated databases; and, it is essential for reasoners to make trust judgements about the information they use over the Semantic Web. As the Web allows information sharing, discovery, aggregation, filtering and flow in an unprecedented manner, it also becomes very difficult to identify, reliably, the original source that produced an information item on the Web. Since the emerging use of provenance in niche applications is undoubtedly demonstrating the benefits of provenance, we contend that provenance can and should reliably be tracked and exploited on the Web, and we survey the necessary foundations to achieve such a vision. Using multiple data sources, we have compiled the largest bibliographical database on provenance so far. This large corpus allows us to analyse emerging trends in the research community. Specifically, using the CiteSpace tool, we identify clusters of papers that constitute research fronts, from which we derive characteristics that we use to structure our foundational framework for provenance on the Web. We note that such an endeavour requires a multidisciplinary approach, since it requires contributions from many computer science sub-disciplines, but also other non-technical fields given the human challenge that is anticipated. To develop our vision, it is necessary to provide a definition of provenance that applies to the Web context. Our conceptual definition of provenance is expressed in terms of processes, and is shown to generalise various definitions of provenance commonly encountered. Furthermore, by bringing realistic distributed systems assumptions, we refine our definition as a query over assertions made by processes. Given that the majority of work on provenance has been undertaken by the database, workflow and e-science communities, we review some of their work, contrasting approaches, and focusing on important topics we believe to be crucial for bringing provenance to the Web, such as abstraction, collections, storage, queries, workflow evolution, semantics and activities involving human interactions. However, provenance approaches developed in the context of databases and workflows essentially deal with closed systems. By that, we mean that workflow or database management systems are in full control of the data they manage, and track their provenance within their own scope, but not beyond. In the context of the Web, a broader approach is required by which chunks of provenance representation can be brought together to describe the provenance of information flowing across multiple systems. This is the specific purpose of the Open Provenance Vision, which is an approach that consists of controlled vocabula...

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Exploiting Provenance to Make Sense of Automated Decisions in Scientific Workflows

Cited by 4 publications

References 7 publications

Making Provenance Work for You

Making Provenance Work for You

Transparent Provenance Derivation for User Decisions

The Foundations for Provenance on the Web

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