Exploiting provenance to make sense of automated decisions in scientific workflows

Paolo Missier; Suzanne Embury; Richard Stapenhurst

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

Exploiting provenance to make sense of automated decisions in scientific workflows

Paolo Missier, Suzanne Embury, Richard Stapenhurst

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Scientific workflows may include automated decision steps, for instance to accept/reject certain data products during the course of an in silico experiment, based on an assessment of their quality. The trustworthiness of these workflows can be enhanced by providing the users with a trace and explanation of the outcome of these decisions. In this paper we present a provenance model that is designed specifically to support this task. The model applies to a particular type of subworkflow that is compiled automatically from a high-level specification of user-defined, quality-based data acceptance criteria. The keys to the effectiveness of the approach are that (i) these sub-workflows follow a predictable pattern structure, (ii) the purpose of their component services is defined using an ontology of Information Quality concepts, and (iii) the conceptual model for provenance is consistent with the ontology structure.

Original language	English
Title of host publication	Provenance and Annotation of Data and Processes - 2nd International Provenance and Annotation Workshop, IPAW 2008, Revised Selected Papers
Editors	Juliana Freire, David Koop, Juliana Freire, Juliana Freire, Luc Moreau
Publisher	Springer Verlag
Pages	174-185
Number of pages	12
ISBN (Print)	9783540899648
DOIs	https://doi.org/10.1007/978-3-540-89965-5_19
Publication status	Published - 2008
Event	2nd International Provenance and Annotation Workshop, IPAW 2008 - Salt Lake City, United States Duration: 17 Jun 2008 → 18 Jun 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5272
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Provenance and Annotation Workshop, IPAW 2008
Country/Territory	United States
City	Salt Lake City
Period	17/06/08 → 18/06/08

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2008.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

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

Cite this

Missier, P., Embury, S., & Stapenhurst, R. (2008). Exploiting provenance to make sense of automated decisions in scientific workflows. In J. Freire, D. Koop, J. Freire, J. Freire, & L. Moreau (Eds.), Provenance and Annotation of Data and Processes - 2nd International Provenance and Annotation Workshop, IPAW 2008, Revised Selected Papers (pp. 174-185). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5272). Springer Verlag. https://doi.org/10.1007/978-3-540-89965-5_19

Missier, Paolo ; Embury, Suzanne ; Stapenhurst, Richard. / Exploiting provenance to make sense of automated decisions in scientific workflows. Provenance and Annotation of Data and Processes - 2nd International Provenance and Annotation Workshop, IPAW 2008, Revised Selected Papers. editor / Juliana Freire ; David Koop ; Juliana Freire ; Juliana Freire ; Luc Moreau. Springer Verlag, 2008. pp. 174-185 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Scientific workflows may include automated decision steps, for instance to accept/reject certain data products during the course of an in silico experiment, based on an assessment of their quality. The trustworthiness of these workflows can be enhanced by providing the users with a trace and explanation of the outcome of these decisions. In this paper we present a provenance model that is designed specifically to support this task. The model applies to a particular type of subworkflow that is compiled automatically from a high-level specification of user-defined, quality-based data acceptance criteria. The keys to the effectiveness of the approach are that (i) these sub-workflows follow a predictable pattern structure, (ii) the purpose of their component services is defined using an ontology of Information Quality concepts, and (iii) the conceptual model for provenance is consistent with the ontology structure.",

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Missier, P, Embury, S & Stapenhurst, R 2008, Exploiting provenance to make sense of automated decisions in scientific workflows. in J Freire, D Koop, J Freire, J Freire & L Moreau (eds), Provenance and Annotation of Data and Processes - 2nd International Provenance and Annotation Workshop, IPAW 2008, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5272, Springer Verlag, pp. 174-185, 2nd International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, United States, 17/06/08. https://doi.org/10.1007/978-3-540-89965-5_19

Exploiting provenance to make sense of automated decisions in scientific workflows. / Missier, Paolo; Embury, Suzanne; Stapenhurst, Richard.
Provenance and Annotation of Data and Processes - 2nd International Provenance and Annotation Workshop, IPAW 2008, Revised Selected Papers. ed. / Juliana Freire; David Koop; Juliana Freire; Juliana Freire; Luc Moreau. Springer Verlag, 2008. p. 174-185 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5272).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Scientific workflows may include automated decision steps, for instance to accept/reject certain data products during the course of an in silico experiment, based on an assessment of their quality. The trustworthiness of these workflows can be enhanced by providing the users with a trace and explanation of the outcome of these decisions. In this paper we present a provenance model that is designed specifically to support this task. The model applies to a particular type of subworkflow that is compiled automatically from a high-level specification of user-defined, quality-based data acceptance criteria. The keys to the effectiveness of the approach are that (i) these sub-workflows follow a predictable pattern structure, (ii) the purpose of their component services is defined using an ontology of Information Quality concepts, and (iii) the conceptual model for provenance is consistent with the ontology structure.

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Missier P, Embury S, Stapenhurst R. Exploiting provenance to make sense of automated decisions in scientific workflows. In Freire J, Koop D, Freire J, Freire J, Moreau L, editors, Provenance and Annotation of Data and Processes - 2nd International Provenance and Annotation Workshop, IPAW 2008, Revised Selected Papers. Springer Verlag. 2008. p. 174-185. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-89965-5_19

Exploiting provenance to make sense of automated decisions in scientific workflows

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this