Provenance and scientific workflows: challenges and opportunities

By Susan B. Davidson, Juliana Freire. 2008

623 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

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In Computer Networks and Communications

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In Information Systems and Management

Citations in the corpus

Understanding artificial intelligence ethics and safety (2019, neutral): “Big Data's disparate impact. Calif. L. Rev., 104, 671. Retrieved from https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/calr104&section=25 Calders, T., & Verwer, S. (2010). Three naive Bayes approaches for discrimination-free classification.”
ProvDB: A System for Lifecycle Management of Collaborative Analysis Workflows (2016, background): “, SQL, Pig Latin, Spark), data provenance at record level can be captured if needed [5,6,8,10,17,22,31,53].”
Artifact-Centric Business Process Models: Brief Survey of Research Results and Challenges (2008, methodology): “Finally, techniques and results from scientific workflow (e.g., [ 14 ]), which is also very data-centric, may have substantial impact on our understanding of artifact-centric business process.”
Provenance in Databases: Why, How, and Where (2009, background): “Although there is some overlap with database and data provenance research, we will restrict attention to work that focuses on provenance within databases, and refer readers interested in workflow provenance to recent surveys [8, 53] and a recent tutorial [29].”
Data X-Ray: A Diagnostic Tool for Data Errors (2015, background): “Roughly, features in our framework are a form of provenance annotations, but these are much simpler than the complex process steps and interactions that workflow provence [2, 19] typically captures and maintains.”
Embedded Data Representations (2017, background): “Systematically maintaining links between data and referents can help viewers interpret data in non-situated cases [8].”
A survey on provenance: What for? What form? What from? (2017, neutral): “instance in [62,74], specializes the previously described”
Fides: Towards a Platform for Responsible Data Science (2017, background): “Provenance propagation and interrogation in Fides will build on a large body of work in the workflows community [3, 15], where provenance is typically coarse-grained and records the sequence of functional steps that led to a result, and in the database community [4, 9, 19], where provenance captures”