The W3C PROV family of specifications for modelling provenance metadata

Missier, Paolo; Belhajjame, Khalid; Cheney, James

doi:10.1145/2452376.2452478

Cited by 158 publications

(114 citation statements)

References 17 publications

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“…Currently, there are two main patterns for data capture from provenance: i) the OPM model [5], with three vertices, five causal relationships, and ii) the PROV model [3], with three main vertices and seven basic relations, plus complementary ones. In this work, the provenance model used was the PROV [3] by its amplitude and greater number of causal relations for knowledge representation.…”

Section: Prov W3cmentioning

confidence: 99%

“…In this work, the provenance model used was the PROV [3] by its amplitude and greater number of causal relations for knowledge representation.…”

Section: Prov W3cmentioning

confidence: 99%

“…The PROV [3] model consists of 12 documents that define their specification. Among the main documents are the PROV-DM, which specifies the data capture model; the PROV-CONSTRAINTS, which is the set of constraints applicable to the data model (PROV-DM), and the PROV-O, an ontology for mapping the data model.…”

Section: Prov W3cmentioning

confidence: 99%

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A Software Framework for Data Provenance

Sirqueira

Viana

Nascimento

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Data provenance refers to the historical record of the derivation of the data, allowing the reproduction of experiments, interpretation of results and identification of problems through the analysis of the processes that originated the data. Data provenance contributes to the evaluation of experiments. This paper presents a framework for data provenance using the W3C provenance data model, called PROV-DM. Such framework aims at contributing to, and facilitating, the collection, storage and retrieval of provenance data through a modeling and storage layer based on PROV-DM, yet is compatible with other representations of PROV such as PROV-O. To demonstrate the utilization of the framework, it was used in an IoT application that performs the gas classification to identify diseases.

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Section: Prov W3cmentioning

confidence: 99%

“…In this work, the provenance model used was the PROV [3] by its amplitude and greater number of causal relations for knowledge representation.…”

Section: Prov W3cmentioning

confidence: 99%

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A Software Framework for Data Provenance

Sirqueira

Viana

Nascimento

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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“…For instance, FAD [6], a functional database programming language designed for easing optimization and parallel execution, provides a whiledo second-order construct for iterating over first-order function calls. Our approach is also tightly-coupled with a relational data provenance model that encompasses W3C PROV [20] and stores additional information such as execution performance and domain data altogether. In order to store domain data, our approach requires the workflow to be instrumented so that the execution engine can capture data and store it in the provenance database during runtime.…”

Section: Introductionmentioning

confidence: 99%

Data-centric iteration in dynamic workflows

Dias

Guerra

Rochinha

et al. 2015

Future Generation Computer Systems

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SummaryDynamic workflows are scientific workflows to support computational science simulations, typically using dynamic processes based on runtime scientific data analyses. They require the ability of adapting the workflow, at runtime, based on user input and dynamic steering. Supporting data-centric iteration is an important step towards dynamic workflows because user interaction with workflows is iterative. However, current support for iteration in scientific workflows is static and does not allow for changing data at runtime. In this paper, we propose a solution based on algebraic operators and a dynamic execution model to enable workflow adaptation based on user input and dynamic steering. We introduce the concept of iteration lineage that makes provenance data management consistent with dynamic iterative workflow changes. Lineage enables scientists to interact with workflow data and configuration at runtime through an API that triggers steering. We evaluate our approach using a novel and real large-scale workflow for uncertainty quantification on a 640-core cluster. The results show impressive execution time savings from 2.5 to 24 days, compared to noniterative workflow execution. We verify that the maximum overhead introduced by our iterative model is less than 5% of execution time. Also, our proposed steering algorithms are very efficient and run in less than 1 millisecond, in the worst-case scenario.

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“…We used Chiron SWMS [12] and its data-centric algebra [13] to query provenance data related to domain data using a specialization of W3C PROV [14]. The work in [11] evolved into defining workflow algebraic operations to control and adapt loop conditions [15].…”

mentioning

confidence: 99%

Raw data queries during data-intensive parallel workflow execution

Silva

Leite

Camata

et al. 2017

Future Generation Computer Systems

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Computer simulations consume and produce huge amounts of raw data files presented in different formats, e.g., HDF5 in computational fluid dynamics simulations. Users often need to analyze domain-specific data based on related data elements from multiple files during the execution of computer simulations. In a raw data analysis, one should identify regions of interest in the data space and retrieve the content of specific related raw data files. Existing solutions, such as FastBit and RAW, are limited to a single raw data file analysis and can only be used after the execution of computer simulations. Scientific Workflow Management Systems (SWMS) can manage the dataflow of computer simulations and register related raw data files at a provenance database. This paper aims to combine the advantages of a dataflow-aware SWMS and the raw data file analysis techniques to allow for queries on raw data file elements that are related, but reside in separate files. We propose a component-based architecture, named as ARMFUL (Analysis of Raw data from Multiple Files) with raw data extraction and indexing techniques, which allows for a direct access to specific elements or regions of raw data space. ARMFUL innovates by using a SWMS provenance database to add a dataflow access path to raw data files. ARMFUL facilitates the invocation of ad-hoc programs and third party tools (e.g., FastBit tool) for raw data analyses. In our experiments, a real parallel computational fluid dynamics is executed, exploring different alternatives of raw data extraction, indexing and analysis.

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The W3C PROV family of specifications for modelling provenance metadata

Cited by 158 publications

References 17 publications

A Software Framework for Data Provenance

A Software Framework for Data Provenance

Data-centric iteration in dynamic workflows

Raw data queries during data-intensive parallel workflow execution

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