tutorial

The W3C PROV family of specifications for modelling provenance metadata

Authors:

Paolo Missier,

Khalid Belhajjame, and

James CheneyAuthors Info & Claims

EDBT '13: Proceedings of the 16th International Conference on Extending Database Technology

March 2013

Pages 773 - 776

https://doi.org/10.1145/2452376.2452478

Published: 18 March 2013 Publication History

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Abstract

Provenance, a form of structured metadata designed to record the origin or source of information, can be instrumental in deciding whether information is to be trusted, how it can be integrated with other diverse information sources, and how to establish attribution of information to authors throughout its history. The PROV set of specifications, produced by the World Wide Web Consortium (W3C), is designed to promote the publication of provenance information on the Web, and offers a basis for interoperability across diverse provenance management systems. The PROV provenance model is deliberately generic and domain-agnostic, but extension mechanisms are available and can be exploited for modelling specific domains. This tutorial provides an account of these specifications. Starting from intuitive and informal examples that present idiomatic provenance patterns, it progressively introduces the relational model of provenance along with the constraints model for validation of provenance documents, and concludes with example applications that show the extension points in use.

References

[1]

P. Agrawal, O. Benjelloun, et al. Trio: a system for data, uncertainty, and lineage. In Proceedings of the 32nd international conference on Very large data bases, VLDB '06, pages 1151--1154. VLDB Endowment, 2006.

Digital Library

Google Scholar

[2]

K. Belhajjame et al. Workflow-centric research objects: First class citizens in scholarly discourse. In Proceedings of Sepublica 2012, pages 1--12, Hersonissos, 2012.

Google Scholar

[3]

P. Buneman, S. Khanna, and W. C. Tan. Why and Where: A Characterization of Data Provenance. In ICDT, pages 316--330, 2001.

Digital Library

Google Scholar

[4]

I. Celino, S. Contessa, M. Corubolo, et al. Linking smart cities datasets with human computation - the case of UrbanMatch. In P. Cudré-Mauroux et al., editors, ISWC, volume 7650 of Lecture Notes in Computer Science, pages 34--49. Springer, 2012.

Digital Library

Google Scholar

[5]

J. Cheney, L. Chiticariu, and W.-C. Tan. Provenance in Databases: Why, How, and Where. Foundations and Trends in Databases, 1:379--474, 2009.

Digital Library

Google Scholar

[6]

J. Cheney, A. Finkelstein, B. Ludaescher, and S. Vansummeren. Principles of Provenance (Dagstuhl Seminar 12091). Dagstuhl Reports, 2(2):84--113, 2012.

Google Scholar

[7]

L. Chiticariu, W.-C. Tan, and G. Vijayvargiya. DBNotes: a post-it system for relational databases based on provenance. In SIGMOD, 2005.

Digital Library

Google Scholar

[8]

V. Cuevas-Vicenttin, S. Dey, and B. Ludaescher. Modeling and querying scientific workflow provenance in the D-OPM. In WORKS. ACM, 2012.

Digital Library

Google Scholar

[9]

E. Deelman, D. Gannon, M. S. Shields, and I. Taylor. Workflows and e-science: An overview of workflow system features and capabilities. Future Generation Comp. Syst., 25(5):528--540, 2009.

Digital Library

Google Scholar

[10]

M. Ebden, T. D. Huynh, L. Moreau, et al. Network analysis on provenance graphs from a crowdsourcing application. In Groth and Frew {12}, pages 168--182.

Digital Library

Google Scholar

[11]

R. Fagin, P. G. Kolaitis, R. J. Miller, and L. Popa. Data exchange: semantics and query answering. Theor. Comput. Sci., 336(1):89--124, 2005.

Digital Library

Google Scholar

[12]

P. T. Groth and J. Frew, editors. 4th International Provenance and Annotation Workshop, IPAW 2012, Santa Barbara, CA, USA, June 19--21, 2012, volume 7525 of Lecture Notes in Computer Science. Springer, 2012.

Crossref

Google Scholar

[13]

G. Karvounarakis, Z. G. Ives, and V. Tannen. Querying data provenance. In SIGMOD Conference, 2010.

Digital Library

Google Scholar

[14]

P. Missier and K. Belhajjame. A PROV encoding for provenance analysis using deductive rules. In Procs. IPAW'12, Santa Barbara, California, 2012. Springer-Verlag, Lecture Notes in Computer Science.

Digital Library

Google Scholar

[15]

P. Missier, S. Soiland-Reyes, S. Owen, et al. Taverna, reloaded. In Procs. SSDBM 2010, volume 6187 of Lecture Notes in Computer Science, pages 471--481, Heidelberg, Germany, 2010. Springer.

Digital Library

Google Scholar

[16]

H. Yang, D. T. Michaelides, C. Charlton, et al. Deep: A provenance-aware executable document system. In Groth and Frew {12}, pages 24--38.

Digital Library

Google Scholar

Cited By

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Pina DChapman AKunstmann Lde Oliveira DMattoso M(2024)DLProv: A Data-Centric Support for Deep Learning Workflow AnalysesProceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning10.1145/3650203.3663337(77-85)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3650203.3663337
Han RZheng MByna STang HDong BDai DChen YKim DHassoun JThorsley D(2024)PROV-IO: A Cross-Platform Provenance Framework for Scientific Data on HPC SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.337455535:5(844-861)Online publication date: May-2024
https://doi.org/10.1109/TPDS.2024.3374555
Kleinsteuber EAl Mustafa TZander FKönig-Ries BBabalou S(2024)Managing Provenance Data in Knowledge Graph Management PlatformsDatenbank-Spektrum10.1007/s13222-023-00463-024:1(43-52)Online publication date: 5-Feb-2024
https://doi.org/10.1007/s13222-023-00463-0
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The W3C PROV family of specifications for modelling provenance metadata

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Reviews

Reviewer: Yingjie Li

Provenance is information about entities, activities, and people involved in producing a piece of data or a thing, which can be used to assess its quality, reliability or trustworthiness. This paper focuses on a new approach using the standard PROV model recommended by the World Wide Web Consortium (W3C) to model provenance. The W3C PROV model defines a core model for provenance representation. Individuals involved in the semantic web, provenance, and ontology field will want to study this work. The first part of the paper provides an intuitive overview of the W3C PROV model with an example involving a complete account of PROV relations with three types of instances: entities, activities, and agents. In PROV, physical, digital, conceptual, or other kinds of things are called entities. [...] Activities are how entities come into existence and how their attributes change to become new entities. [...] An agent takes a role in an activity such that the agent can be assigned some degree of responsibility for the activity taking place. [1] In addition, the validity of the provenance statements is defined with reference to a set of constraints that the statements must satisfy. For instance, when two entities use the predicate prov:wasDerivedFrom , it implies that the first entity precedes the second one. The second part of the paper presents a number of applications that use the PROV model to capture provenance information. In Dictionary, the PROV model asserts the membership of a word in a dictionary and records the change (insertion and removal) history of the words. In Scientific Workflows, the PROV model captures information about the data products used and generated by the steps that compose the workflows. As a result, people can easily debug workflows and reproduce the workflow results. In Executable Documents, the PROV model captures the provenance of each research object to trace its evolution over time. In Smart Cities, the PROV model records the provenance information about citizens and their contributions to assist in the verification of collected data. The paper would have been more complete if the authors had provided a deeper analysis of how the applications apply the PROV model in terms of provenance modeling and querying. Online Computing Reviews Service

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Information & Contributors

Information

Published In

EDBT '13: Proceedings of the 16th International Conference on Extending Database Technology

March 2013

793 pages

ISBN:9781450315975

DOI:10.1145/2452376

General Chair:
Giovanna Guerrini
Università di Genova, Italy
,
Program Chair:
Norman W. Paton
University of Manchester, UK

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

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EDBT/ICDT '13

EDBT/ICDT '13: Joint 2013 EDBT/ICDT Conferences

March 18 - 22, 2013

Genoa, Italy

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Overall Acceptance Rate 7 of 10 submissions, 70%

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Pina DChapman AKunstmann Lde Oliveira DMattoso M(2024)DLProv: A Data-Centric Support for Deep Learning Workflow AnalysesProceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning10.1145/3650203.3663337(77-85)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3650203.3663337
Han RZheng MByna STang HDong BDai DChen YKim DHassoun JThorsley D(2024)PROV-IO: A Cross-Platform Provenance Framework for Scientific Data on HPC SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.337455535:5(844-861)Online publication date: May-2024
https://doi.org/10.1109/TPDS.2024.3374555
Kleinsteuber EAl Mustafa TZander FKönig-Ries BBabalou S(2024)Managing Provenance Data in Knowledge Graph Management PlatformsDatenbank-Spektrum10.1007/s13222-023-00463-024:1(43-52)Online publication date: 5-Feb-2024
https://doi.org/10.1007/s13222-023-00463-0
SOYGAZİ FTUĞLULAR TDİKENELLİ O(2023)Data Management and Ontology Development for Provenance-Aware Organizations in Linked Data SpaceEuropean Journal of Technic10.36222/ejt.1402149Online publication date: 26-Dec-2023
https://doi.org/10.36222/ejt.1402149
Johns MMeurers TWirth FHaber AMüller AHalilovic MBalzer FPrasser F(2023)Data Provenance in Biomedical Research: Scoping ReviewJournal of Medical Internet Research10.2196/4228925(e42289)Online publication date: 27-Mar-2023
https://doi.org/10.2196/42289
Psallidas FAgrawal ASugunan CIbrahim KKaranasos KCamacho-Rodríguez JFloratou ACurino CRamakrishnan R(2023)OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance from Database Query Event LogsProceedings of the VLDB Endowment10.14778/3611540.361155516:12(3662-3675)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.14778/3611540.3611555
Paton NChen JWu Z(2023)Dataset Discovery and Exploration: A SurveyACM Computing Surveys10.1145/362652156:4(1-37)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3626521
Bartley BBeal JRogers MBryce DGoldman RKeller BLee PBiggers VNowak JWeston M(2023)Building an Open Representation for Biological ProtocolsACM Journal on Emerging Technologies in Computing Systems10.1145/360456819:3(1-21)Online publication date: 23-Jun-2023
https://dl.acm.org/doi/10.1145/3604568
Pan BStakhanova NRay S(2023)Data Provenance in Security and PrivacyACM Computing Surveys10.1145/359329455:14s(1-35)Online publication date: 22-Apr-2023
https://dl.acm.org/doi/10.1145/3593294
Psallidas FLeszczynski MNamaki MFloratou AAgrawal AKaranasos KKrishnan SSubotic PWeimer MWu YZhu YDas SPandis ISelçuk Candan KAmer-Yahia S(2023)Demonstration of Geyser: Provenance Extraction and Applications over Data Science ScriptsCompanion of the 2023 International Conference on Management of Data10.1145/3555041.3589717(123-126)Online publication date: 4-Jun-2023
https://dl.acm.org/doi/10.1145/3555041.3589717
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Recommendations

Modeling uncertain provenance and provenance of uncertainty in W3C PROV

Modeling Information Diffusion in Social Media as Provenance with W3C PROV

An Ecore Metamodel for the W3C PROV Provenance Data Model

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