Linking Abstract Plans of Scientific Experiments to their Corresponding Execution Traces

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Provenance describes the creation, manipulation and delivery processes of scientific results; and has become a crucial requirement for debugging, understanding, inspecting and reproducing the outcomes of scientific publications. Scientific experiments, in particular computational workflows, often include provenance collection mechanisms that link execution traces to their respective planned specifications. Such provenance traces are typically very fine-grained, and may quickly become too complex or difficult for humans to interpret. In this paper we describe our approach to represent workflow plans and provenance at different levels of abstraction. We describe EP-Plan, a W3CPROV ontology extension and we illustrate our approach with a use case using the WINGS workflow system.
Original languageEnglish
Title of host publicationProceedings of the Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019)
PublisherCEUR-WS
Number of pages4
Publication statusAccepted/In press - 21 Oct 2019
EventThird International Workshop on Capturing Scientific Knowledge (SciKnow 2019) - Los Angeles, United States
Duration: 19 Nov 201919 Nov 2019

Workshop

WorkshopThird International Workshop on Capturing Scientific Knowledge (SciKnow 2019)
CountryUnited States
CityLos Angeles
Period19/11/1919/11/19

Fingerprint

Ontology
Specifications
Experiments

Keywords

  • Plan
  • Scientific workflows
  • provenance
  • abstractions

Cite this

Markovic, M., Garijo, D., & Edwards, P. (Accepted/In press). Linking Abstract Plans of Scientific Experiments to their Corresponding Execution Traces. In Proceedings of the Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019) CEUR-WS.

Linking Abstract Plans of Scientific Experiments to their Corresponding Execution Traces. / Markovic, Milan; Garijo, Daniel; Edwards, Peter.

Proceedings of the Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019). CEUR-WS, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Markovic, M, Garijo, D & Edwards, P 2019, Linking Abstract Plans of Scientific Experiments to their Corresponding Execution Traces. in Proceedings of the Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019). CEUR-WS, Third International Workshop on Capturing Scientific Knowledge (SciKnow 2019), Los Angeles, United States, 19/11/19.
Markovic M, Garijo D, Edwards P. Linking Abstract Plans of Scientific Experiments to their Corresponding Execution Traces. In Proceedings of the Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019). CEUR-WS. 2019
Markovic, Milan ; Garijo, Daniel ; Edwards, Peter. / Linking Abstract Plans of Scientific Experiments to their Corresponding Execution Traces. Proceedings of the Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019). CEUR-WS, 2019.
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abstract = "Provenance describes the creation, manipulation and delivery processes of scientific results; and has become a crucial requirement for debugging, understanding, inspecting and reproducing the outcomes of scientific publications. Scientific experiments, in particular computational workflows, often include provenance collection mechanisms that link execution traces to their respective planned specifications. Such provenance traces are typically very fine-grained, and may quickly become too complex or difficult for humans to interpret. In this paper we describe our approach to represent workflow plans and provenance at different levels of abstraction. We describe EP-Plan, a W3CPROV ontology extension and we illustrate our approach with a use case using the WINGS workflow system.",
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note = "ACKNOWLEDGMENTS The work described in this paper was funded by the award made by the RCUK Digital Economy programme to the University of Aberdeen (EP/N028074/1), a SICSAPECE travel award, the Defense Advanced Research Projects Agency with award W911NF-18-10027, the SIMPLEX program with award W911NF-15-1-0555 and from the National Institutes of Health under awards 1U01CA196387 and 1R01GM117097.",
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