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ACM SIGMOD Record, 2019 Research into data provenance has been active for almost twenty years. What has it delivered and where will it go next? What practical impact has it had and what might it have? We provide speculative answers to these questions which may be somewhat biased by our initial motivation for studying the topic: the need for provenance information in curated ...
Buneman, Peter; id_orcid 0009-0004-9056-8587 +1 more
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Querying data provenance [PDF]
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010 Many advanced data management operations (e.g., incremental maintenance, trust assessment, debugging schema mappings, keyword search over databases, or query answering in probabilistic databases), involve computations that look at how a tuple was produced, e.g., to determine its score or existence.
Grigoris Karvounarakis +2 more
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Frontiers in Neuroinformatics, 2022 Computational tools can transform the manner by which neuroscientists perform their experiments. More than helping researchers to manage the complexity of experimental data, these tools can increase the value of experiments by enabling reproducibility ...
Margarita Ruiz-Olazar +6 more
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BioMedInformatics, 2022 The increase in speed, reliability, and cost-effectiveness of high-throughput sequencing has led to the widespread clinical application of genome (WGS), exome (WXS), and transcriptome analysis.
Hindrik HD Kerstens +11 more
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A Big Data Provenance Model for Data Security Supervision Based on PROV-DM Model
IEEE Access, 2020 Nowadays, big data has become a hot research topic. It gives fresh impetus to the economic and social development. However, the huge value of big data also makes it the focus of attacks. Big data security incidents occur frequently in recent years.
Yuanzhao Gao, Xingyuan Chen, Xuehui Du
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Security-Aware Provenance for Transparency in IoT Data Propagation
IEEE Access, 2023 A successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of data collected from numerous sources.
Fariha Tasmin Jaigirdar +3 more
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Provenance Network Analytics [PDF]
Data Mining and Knowledge Discovery, 2018 Provenance network analytics is a novel data analytics approach that helps infer properties of data, such as quality or importance, from their provenance. Instead of analysing application data, which are typically domain-dependent, it analyses the data's provenance as represented using the World Wide Web Consortium's domain-agnostic PROV data model ...
Trung Dong Huynh +4 more
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A Model To Capture Interaction Between Data Provenance And Workflow Provenance
JISR on Computing, 2014 Provenance means origin. There are two types of Provenance namely Data Provenance and Workflow Provenance. Data Provenance refers to the process of recording and tracking the source or origin of data while workflow provenance means history of workflows ...
Zeeshan Ahmed, Syed Saif ur Rahman
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, 2022 In constraint languages for RDF graphs, such as ShEx and SHACL, constraints on nodes and their properties are known as “shapes”. Using SHACL, we propose in this paper the notion of neighborhood of a node v satisfying a given shape in a graph G. This neighborhood is a subgraph of G, and provides data provenance of v for the given shape.
Thomas Delva +3 more
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Using Introspection to Collect Provenance in R
Informatics, 2018 Data provenance is the history of an item of data from the point of its creation to its present state. It can support science by improving understanding of and confidence in data. RDataTracker is an R package that collects data provenance from R scripts (
Barbara Lerner, Emery Boose, Luis Perez
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