Article

Retrospective provenance without a runtime provenance recorder

Authors:

Timothy McPhillips,

Khalid Belhajjame,

Bertram LudäscherAuthors Info & Claims

TaPP'15: Proceedings of the 7th USENIX Conference on Theory and Practice of Provenance

Page 1

Published: 08 July 2015 Publication History

Abstract

The YesWorkflow (YW) toolkit aims to provide users of scripting languages such as Python, Perl, and R with many of the benefits of scientific workflow automation. YW requires neither the use of a workflow engine nor the overhead of adapting or instrumenting code to run in such a system. Instead, YW enables scientists to annotate their scripts with special comments that reveal the main computational blocks and dataflow dependencies otherwise implicit in scripts. YW tools extract and analyze these comments, represent scripts in terms of entities based on a typical scientific workflow model, and provide graphical workflow views (i.e., prospective provenance) of scripts. In this paper, we present a new extension of YW for inferring retrospective provenance from script executions without relying on a runtime provenance recorder. Instead we exploit the common practice of scientists to embed important pieces of provenance in directory structures and file names. For such "provenance-friendly" data organizations, we offer a new annotation mechanism based on URI templates. YW uses these to link conceptual-level prospective provenance with data files created at runtime, resulting in a powerful, integrated model of prospective and retrospective provenance. We present scientifically meaningful retrospective provenance queries for investigating an execution of a data acquisition workflow implemented as a Python script, and show how these queries can be evaluated using the YW toolkit.

References

[1]

P. Amstutz et al. Common Workflow Language. github.com/common-workflow-language, 2015.

[2]

I. Altintas, O. Barney, and E. Jaeger-Frank. Provenance collection support in the Kepler scientific workflow system. In IPAW, 2006.

Digital Library

[3]

R. K. Bocinsky and T. A. Kohler. A 2,000-Year reconstruction of the rain-fed maize agricultural niche in the US Southwest. Nature Communications, 5, 2014.

[4]

S. Bowers, T. M. McPhillips, and B. Ludäscher. Declarative Rules for Inferring Fine-Grained Data Provenance from Scientific Workflow Execution Traces. In Intl. Provenance and Annotation Workshop (IPAW), pp. 82-96, 2012.

Digital Library

[5]

S. Bowers, T. McPhillips, S. Riddle, M. K. Anand, and B. Ludäscher. Kepler/pPOD: Scientific workflow and provenance support for assembling the tree of life. In IPAW, 2008.

Digital Library

[6]

S. Bowers, T. McPhillips, M. Wu, and B. Ludäscher. Project histories: Managing data provenance across collection-oriented scientific workflow runs. In Data Integration in the Life Sciences (DILS), volume 4544 of LNCS, pp. 122-138. Springer, 2007. Preprint.

Digital Library

[7]

S. Bowers. Scientific workflow, provenance, and data modeling challenges and approaches. Journal on Data Semantics, 1(1):19-30, 2012.

[8]

S. B. Davidson, S. C. Boulakia, A. Eyal, B. Ludäscher, T. M. McPhillips, S. Bowers, M. K. Anand, and J. Freire. Provenance in Scientific Workflow Systems. IEEE Data Eng. Bull., 30(4):44-50, 2007.

[9]

S. Dey, K. Belhajjame, D. Koop, M. Raul, and B. Ludäscher. Linking Prospective and Retrospective Provenance for Scripts. In Intl. Workshop on Theory and Practice of Provenance (TaPP), 2015.

[10]

S. Frazer et al. Workflow Description Language. github.com/broadinstitute/wdl, 2015.

[11]

J. Freire, D. Koop, F. S. Chirigati, and C. T. Silva. Reproducibility using vistrails. Implementing Reproducible Research, page 33, 2014.

[12]

J. Frew, D. Metzger, and P. Slaughter. Automatic capture and reconstruction of computational provenance. Concurrency and Computation: Practice and Experience, 20(5):485-496, 2008.

Digital Library

[13]

C. Gandrud. Reproducible Research with R and R Studio. CRC Press, 2013.

[14]

C. Hedeler, K. Belhajjame, A. A. Fernandes, S. M. Embury, and N. W. Paton. Dimensions of Dataspaces. In Dataspace: The Final Frontier, pp. 55-66. Springer, 2009.

Digital Library

[15]

B. Ludäscher, I. Altintas, C. Berkley, D. Higgins, E. Jaeger, M. Jones, E. A. Lee, J. Tao, and Y. Zhao. Scientific workflow management and the Kepler system. Concurrency and Computation: Practice and Experience, 18(10):1039-1065, 2006.

[16]

L. Murta, V. Braganholo, F. Chirigati, D. Koop, and J. Freire. noWorkflow: Capturing and Analyzing Provenance of Scripts. In Intl. Provenance and Annotation Workshop (IPAW), 2014.

[17]

T. McPhillips, S. Bowers, and B. Ludäscher. yesworkflow.org/yw-tapp-15-recon, 2015.

[18]

P. Missier, K. Belhajjame, J. Zhao, M. Roos, and C. Goble. Data lineage model for Taverna workflows with lightweight annotation requirements. In IPAW, 2008.

Digital Library

[19]

T. McPhillips, T. Song, T. Kolisnik, S. Aulenbach, K. Belhajjame, K. Bocinsky, Y. Cao, J. Cheney, F. Chirigati, S. Dey, J. Freire, C. Jones, J. Hanken, K. W. Kintigh, T. A. Kohler, D. Koop, J. A. Macklin, P. Missier, M. Schildhauer, C. Schwalm, Y. Wei, M. Bieda, and B. Ludäscher. YesWorkflow: A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts. International Journal of Digital Curation (IJDC), 10(1):298-313, 2015. Presented at IDCC'15, 30 Euston Square, London, UK.

[20]

V. Stodden, F. Leisch, and R. D. Peng. Implementing reproducible research. CRC Press, 2014.

[21]

Y. Tsai, S. E. McPhillips, A. González, T. M. McPhillips, D. Zinn, A. E. Cohen, M. D. Feese, D. Bushnell, T. Tiefenbrunn, C. D. Stout, et al. AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery. Acta Cryst, 500:69, 2013.

[22]

M. A. Vaz Salles, J.-P. Dittrich, S. K. Karakashian, O. R. Girard, and L. Blunschi. iTrails: pay-as-you-go information integration in dataspaces. In VLDB, pp. 663-674, 2007.

Digital Library

[23]

K. Wolstencroft, R. Haines, D. Fellows, A. Williams, D. Withers, S. Owen, S. Soiland-Reyes, I. Dunlop, A. Nenadic, P. Fisher, et al. The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Research, 2013.

[24]

YesWorkflow project site and README. yesworkflow.org/yw, 2015.

[25]

Y. Zhao, M. Wilde, and I. Foster. Applying the virtual data provenance model. In Intl. Provenance and Annotation Workshop (IPAW), 2006.

Digital Library

Cited By

Sonnekalb THeinze TKurnatowski LSchreiber AGonzalez-Barahona JPacker HGalster MMirakhorli MWilliams L(2020)Towards automated, provenance-driven security audit for git-based repositories: applied to germany's corona-warn-app: vision paperProceedings of the 3rd ACM SIGSOFT International Workshop on Software Security from Design to Deployment10.1145/3416507.3423190(15-18)Online publication date: 9-Nov-2020
https://dl.acm.org/doi/10.1145/3416507.3423190
Pimentel JFreire JMurta LBraganholo V(2019)A Survey on Collecting, Managing, and Analyzing Provenance from ScriptsACM Computing Surveys10.1145/331195552:3(1-38)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3311955
Sarikhani MWendelborn A(2018)Mechanisms for provenance collection in scientific workflow systemsComputing10.1007/s00607-017-0578-1100:5(439-472)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s00607-017-0578-1
Show More Cited By

Recommendations

Prospective and Retrospective Provenance Collection in Scientific Workflow Environments
SCC '10: Proceedings of the 2010 IEEE International Conference on Services Computing

Provenance, a record of the derivation history of scientific results, is critical for scientific workflows to support reproducibility, result interpretation, and problem diagnosis. Both prospective provenance, which captures an abstract workflow ...
Capturing and querying workflow runtime provenance with PROV: a practical approach
EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

Scientific workflows are commonly used to model and execute large-scale scientific experiments. They represent key resources for scientists and are enacted and managed by Scientific Workflow Management Systems (SWfMS). Each SWfMS has its particular ...
Provenance opportunities for WS-VLAM: an exploration of an e-science and an e-business approach
WORKS '11: Proceedings of the 6th workshop on Workflows in support of large-scale science

Scientific applications are frequently modeled as a workflow that is executed under the control of a workflow management system. One crucial requirement during the execution is the validation of the generated results and the traceability of the ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

TaPP'15: Proceedings of the 7th USENIX Conference on Theory and Practice of Provenance

July 2015

14 pages

Program Chairs:
Paolo Missier
Newcastle University
,
Jun Zhao
Lancaster University

Publisher

USENIX Association

United States

Publication History

Published: 08 July 2015

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 10 of 17 submissions, 59%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Sonnekalb THeinze TKurnatowski LSchreiber AGonzalez-Barahona JPacker HGalster MMirakhorli MWilliams L(2020)Towards automated, provenance-driven security audit for git-based repositories: applied to germany's corona-warn-app: vision paperProceedings of the 3rd ACM SIGSOFT International Workshop on Software Security from Design to Deployment10.1145/3416507.3423190(15-18)Online publication date: 9-Nov-2020
https://dl.acm.org/doi/10.1145/3416507.3423190
Pimentel JFreire JMurta LBraganholo V(2019)A Survey on Collecting, Managing, and Analyzing Provenance from ScriptsACM Computing Surveys10.1145/331195552:3(1-38)Online publication date: 18-Jun-2019
https://dl.acm.org/doi/10.1145/3311955
Sarikhani MWendelborn A(2018)Mechanisms for provenance collection in scientific workflow systemsComputing10.1007/s00607-017-0578-1100:5(439-472)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s00607-017-0578-1
Erickson JSheehan JBennett KMcguinness D(2016)Addressing Scientific Rigor in Data Analytics Using Semantic WorkflowsProceedings of the 6th International Workshop on Provenance and Annotation of Data and Processes - Volume 967210.5555/3090188.3090211(187-190)Online publication date: 7-Jun-2016
https://dl.acm.org/doi/10.5555/3090188.3090211
Pimentel JDey SMcphillips TBelhajjame KKoop DMurta LBraganholo VLudäscher B(2016)Yin & YangProceedings of the 6th International Workshop on Provenance and Annotation of Data and Processes - Volume 967210.5555/3090188.3090205(161-165)Online publication date: 7-Jun-2016
https://dl.acm.org/doi/10.5555/3090188.3090205

View Options

View options

Figures

Tables

Media

View Table of Conten