survey

Provenance Analytics for Workflow-Based Computational Experiments: A Survey

Authors:

Wellington Oliveira,

Daniel De Oliveira,

Vanessa BraganholoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 3

Article No.: 53, Pages 1 - 25

https://doi.org/10.1145/3184900

Published: 23 May 2018 Publication History

Abstract

Until not long ago, manually capturing and storing provenance from scientific experiments were constant concerns for scientists. With the advent of computational experiments (modeled as scientific workflows) and Scientific Workflow Management Systems, produced and consumed data, as well as the provenance of a given experiment, are automatically managed, so provenance capturing and storing in such a context is no longer a major concern. Similarly to several existing big data problems, the bottom line is now on how to analyze the large amounts of provenance data generated by workflow executions and how to be able to extract useful knowledge of this data. In this context, this article surveys the current state of the art on provenance analytics by presenting the key initiatives that have been taken to support provenance data analysis. We also contribute by proposing a taxonomy to classify elements related to provenance analytics.

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Daga EGroth P(2024)Data journeys: Explaining AI workflows through abstractionSemantic Web10.3233/SW-23340715:4(1057-1083)Online publication date: 4-Oct-2024
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Auge TFeistel SEkaputra FKlettke MJürgensmann SMichels EWaltersdorfer L(2024)Towards an Integrated Provenance Framework: A Scenario for Marine Data2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW61312.2024.00071(597-601)Online publication date: 8-Jul-2024
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Landeta-López PGuevara-Vega C(2024)Computational Experiments in Computer Science Research: A Literature SurveyIEEE Access10.1109/ACCESS.2024.345880812(132254-132270)Online publication date: 2024
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Index Terms

Provenance Analytics for Workflow-Based Computational Experiments: A Survey
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Data provenance
  2. Information systems applications
    1. Process control systems

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 51, Issue 3

May 2019

796 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3212709

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Copyright © 2018 ACM.

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Publication History

Published: 23 May 2018

Accepted: 01 January 2018

Revised: 01 June 2017

Received: 01 December 2016

Published in CSUR Volume 51, Issue 3

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Daga EGroth P(2024)Data journeys: Explaining AI workflows through abstractionSemantic Web10.3233/SW-23340715:4(1057-1083)Online publication date: 4-Oct-2024
https://doi.org/10.3233/SW-233407
Auge TFeistel SEkaputra FKlettke MJürgensmann SMichels EWaltersdorfer L(2024)Towards an Integrated Provenance Framework: A Scenario for Marine Data2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW61312.2024.00071(597-601)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSPW61312.2024.00071
Landeta-López PGuevara-Vega C(2024)Computational Experiments in Computer Science Research: A Literature SurveyIEEE Access10.1109/ACCESS.2024.345880812(132254-132270)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3458808
Alencar VKohwalter TBraganholo Vda Silva JMurta L(2024)Prov-Dominoes: An approach for knowledge discovery from provenance dataExpert Systems with Applications10.1016/j.eswa.2023.123030245(123030)Online publication date: Jul-2024
https://doi.org/10.1016/j.eswa.2023.123030
Costa‐Pérez XSciancalepore VZanzi LAlbanese A(2023)Blockchain for Mobile NetworksBlockchains10.1002/9781119781042.ch7(185-213)Online publication date: 8-Sep-2023
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Samuel SKönig-Ries B(2022)A collaborative semantic-based provenance management platform for reproducibilityPeerJ Computer Science10.7717/peerj-cs.9218(e921)Online publication date: 10-Mar-2022
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Gadelha LHohmuth MZulfiqar MSchone DSamuel SSorokina MSteinbeck CKonig-Ries B(2022)Toward a Framework for Integrative, FAIR, and Reproducible Management of Data on the Dynamic Balance of Microbial Communities2022 IEEE 18th International Conference on e-Science (e-Science)10.1109/eScience55777.2022.00080(443-449)Online publication date: Oct-2022
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Alam KRoy B(2022)Challenges of Provenance in Scientific Workflow Management Systems2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS)10.1109/WORKS56498.2022.00007(10-18)Online publication date: Nov-2022
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Block JEsmaeili SRagan EGoodall JRichardson G(2022)The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis ScenarioIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209495(1-11)Online publication date: 2022
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da Costa UEspinosa-Oviedo JMusicante MVargas-Solar GZechinelli-Martini J(2022)Using Provenance in Data Analytics for Seismology: Challenges and DirectionsNew Trends in Database and Information Systems10.1007/978-3-031-15743-1_29(311-322)Online publication date: 29-Aug-2022
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