A survey on provenance: What for? What form? What from?
Integrity signals
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In interconnected systems, data flow drives everything, integrating systems and bringing their functionality.
“Importantly, in these interconnected systems data flow drives everything, in that the flow of data between, through, and across systems works to integrate them and brings their functionality [22].”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [22]
The general opacity of interconnections between systems, coupled with the general lack of technical means for tracking data flow at such a scale, poses accountability issues.
“The general opacity of the interconnections between systems, coupled with the general lack of technical means for tracking data flow at such a scale, therefore poses accountability issues, as it becomes difficult to discern the technical components involved, and then who is responsible for these [22], [23].”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [22]
Although provenance has been considered for some specific compliance aspects, the broader accountability considerations have yet to be explored in depth.
“Though, as mentioned, provenance has been considered for some specific compliance aspects (see for e.g.[22], [23], [27]- [29]), the broader accountability considerations have yet to be explored in depth.”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [22]
Proactive measures could allow users to set policy that constrains particular information flows or keep users informed of changes in circumstance.
“That said, proactive measures could also be employed to allow users to set policy that constrain particular information flows [22], or keep users informed of any change in circumstance.”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [22]
More information regarding the nature of systems could allow those building and deploying systems to improve their quality and identify points of failure, while empowering others by offering more insight into systems and data beyond their direct control.
“Generally speaking, more information regarding the nature of systems could allow those building and deploying systems to improve their quality and to identify points of failure, while empowering others by offering more insight into the nature of the systems and data that is beyond their direct control [4], [22], [23].”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [22]
The importance of technical audit and investigation concerns will only increase as IoT and smart cities become a reality.
“The importance of such concerns will only increase as the visions of the IoT and smart cities become a reality, due to their complex and highly-interconnected nature and also their potential for emergent functionality and real-time reconfiguration [4], [22].”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [22]
Reactive, event-based mechanisms could automatically take actions to assist compliance and obligation management.
“Reactive, event-based mechanisms could also automatically take actions to assist compliance and obligation management [22].”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [22]
There is a clear requirement for means that assure and ensure the integrity of the capture regime, recorded data, query mechanism, and other compliance mechanisms, and maintain that integrity throughout any pipeline.
“In all, there is a clear requirement for means that assure and ensure the integrity of the capture regime, recorded data, the query mechanism, and any other compliance mechanisms (such as policy enforcement regimes) that leverage provenance data, and also to ensure that this integrity is maintained throughout any pipeline [22], [23], [48].”
From Decision Provenance: Harnessing Data Flow for Accountable Systems (2019) · cites this paper as [22]
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On Provenance of Queries on Semantic Web Data
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Distributed time-aware provenance
Zhou, W., Mapara, S., Ren, Y., Li, Y., Haeberlen, A., Ives, Z. G., Loo, B. T., & Sherr, M. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2535568.2448939 · Cited by 46 · see citing works
Collaborative data sharing via update exchange and provenance
Karvounarakis, G., Green, T. J., Ives, Z. G., & Tannen, V. (2013) · ACM Transactions on Database Systems · doi:10.1145/2500127 · Cited by 45 · see citing works
A graph model of data and workflow provenance
Acar, U. A., Buneman, P., Cheney, J., den Bussche, J. V., Kwaśnikowska, N., & Vansummeren, S. (2010) · doi:10.5555/1855795.1855803 · Cited by 44 · see citing works
Provenance-Based Reproducibility in the Semantic Web
Moreau, L. (2011) · Journal of Web Semantics · doi:10.1016/j.websem.2011.03.001 · Cited by 42 · see citing works
Approximated Summarization of Data Provenance
Ainy, E., Bourhis, P., Davidson, S. B., Deutch, D., & Milo, T. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806429 · Cited by 41 · see citing works
PROV-O-Viz - Understanding the Role of Activities in Provenance
Hoekstra, R., & Groth, P. T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16462-5_18 · Cited by 40 · see citing works
Collecting Provenance via the Xen Hypervisor
Macko, P., Chiarini, M., & Seltzer, M. (2011) · Cited by 39 · see citing works
High-Level Why-Not Explanations using Ontologies
ten Cate, B., Civili, C., Sherkhonov, E., & Tan, W.-C. (2015) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/2745754.2745765 · Cited by 39 · see citing works
Helping users recall their reasoning process
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Provenance for MapReduce-based data-intensive workflows
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Transforming provenance using redaction
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Towards automated collection of application-level data provenance
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A roadmap for privacy-enhanced secure data provenance
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A formal model of provenance in distributed systems
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Glavic, B., Alonso, G., Miller, R. J., & Haas, L. M. (2010) · Proceedings of the VLDB Endowment · doi:10.14778/1920841.1921003 · Cited by 32 · see citing works
Provenance Information Model of Karma Version 3
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Explaining outputs in modern data analytics
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On the Limitations of Provenance for Queries With DifferencePreprint
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Using Provenance to Support Real-Time Collaborative Design of Workflows
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Detecting common scientific workflow fragments using templates and execution provenance
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Large-Scale Distributed Storage System for Business Provenance
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Efficient Computation of Polynomial Explanations of Why-Not Questions
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Provenance Explorer – Customized Provenance Views Using Semantic Inferencing
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Computer Software and Applications Conference Workshops (COMPSACW)
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StarFlow: A Script-Centric Data Analysis Environment
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The perm provenance management system in action
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Provenance management in Swift
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Reasoning about Explanations for Negative Query Answers in DL-Lite
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Observing SQL queries in their natural habitat
Grust, T., & Rittinger, J. (2013) · ACM Transactions on Database Systems · doi:10.1145/2445583.2445586 · Cited by 23 · see citing works
Mapping the NRC Dataflow Model to the Open Provenance Model
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Enhancing and abstracting scientific workflow provenance for data publishing
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First-Order Provenance GamesPreprint
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Proceedings of the 2nd Conference on Theory and Practice of Provenance
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Immutably answering Why-Not questions for equivalent conjunctive queries
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Yin & Yang: Demonstrating Complementary Provenance from noWorkflow & YesWorkflow
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Analysis Preservation in ATLAS
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PROB: A tool for Tracking Provenance and Reproducibility of Big Data Experiments
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DataONE: A Data Federation with Provenance Support
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2017 Workshop on Theory and Practice of Provenance (TaPP 2017)
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Improving Workflow Fault Tolerance through Provenance-Based Recovery
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ProvAbs: Model, Policy, and Tooling for Abstracting PROV Graphs
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A Hybrid Approach to Answering Why-Not Questions on Relational Query Results
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Self-citationTowards Multi-level Provenance Reconstruction of Information Diffusion on Social Media
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Linking Prospective and Retrospective Provenance in Scripts
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Self-citationReenactment for Read-Committed Snapshot Isolation
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Collaborative data sharing via update exchange and provenance
Karvounarakis, G., Green, T. J., Ives, Z. G., et al. (2013) · ACM Transactions on Database Systems · doi:10.1145/2508020.2500127 · Cited by 17 · see citing works
Fine-Grained Provenance Collection over Scripts Through Program Slicing
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Analyzing Provenance Across Heterogeneous Provenance Graphs
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Metadata and Provenance Management
Deelman, E., Berriman, G. B., Chervenak, A., et al. (2009) · Chapman & Hall/CRC computational science series/Chapman & Hall/CRC computational science · doi:10.1201/9781420069815-c12 · Cited by 15 · see citing works
FlexIQ: A flexible interactive Querying Framework by Exploiting the Skyline Operator
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Data Provenance and Reproducibility in Grid Based Scientific Workflows
Tylissanakis, G., & Cotronis, Y. (2009) · doi:10.1109/gpc.2009.16 · Cited by 13 · see citing works
Towards constraint provenance games
Riddle, S., Köhler, S., & Ludäscher, B. (2014) · Cited by 13 · see citing works
A Model for Sharing of Confidential Provenance Information in a Query Based System
Nagappan, M., & Vouk, M. A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89965-5_8 · Cited by 12 · see citing works
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Workflows to open provenance graphs, round-trip
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Advanced Case Management Enabled by Business Provenance
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Bidoít, N., Herschel, M., & Tzompanaki, K. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2824032.2824116 · Cited by 12 · see citing works
Self-citationOlston, C., & Reed, B. C. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/3402755.3402758 · Cited by 12 · see citing works
A provenance framework for data-dependent process analysis
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selP: Selective tracking and presentation of data provenance
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Prov2ONE: An Algorithm for Automatically Constructing ProvONE Provenance Graphs
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A provenance model for key-value systems
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Zhang, J., & Jagadish, H. V. (2010) · International Conference on Extending Database Technology (EDBT) · doi:10.1145/1739041.1739080 · Cited by 10 · see citing works
Provenance-based dictionary refinement in information extraction
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Interoperability for provenance-aware databases using PROV and JSON
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Easy Access to Provenance: An Essential Step Towards Trust on the Web
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Amann, B., Constantin, C., Caron, C., et al. (2013) · doi:10.1145/2457317.2457367 · Cited by 8 · see citing works
Provenance for SQL through abstract interpretation
Müller, T., & Grust, T. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2824032.2824089 · Cited by 8 · see citing works
Explanations for Skyline Query Results
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Leveraging Metadata in No SQL Storage Systems
Alkhaldi, A., Gupta, I., Raghavan, V., & Ghosh, M. (2015) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2015.18 · Cited by 7 · see citing works
Herschel, M., & Eichelberger, H. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398743 · Cited by 7 · see citing works
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Karsai, L., Fekete, A. D., Kay, J., et al. (2016) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/2939502.2939508 · Cited by 7 · see citing works
Obscuring Provenance Confidential Information via Graph Transformation
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Visualizing the Evolution of Module Workflows
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Language-integrated provenance
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Towards secure user-space provenance capture
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POLYTICS: Provenance-Based Analytics of Data-Centric Applications
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Flagged first, then by citations
Cited by
Sorted by citations
Shneiderman, B. (2020) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3419764 · Cited by 768 · see citing works
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612 · see citing works
“By tracking the data lineage at the highest resolution, data provenance can enhance the transparency, reproducibility, and accountability of an AI system [154, 172].”
BackgroundInfluentialKroll, J. A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0084 · Cited by 132 · see citing works
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359221 · Cited by 111 · see citing works
Decision Provenance: Harnessing Data Flow for Accountable Systems
Singh, J., Cobbe, J., & Norval, C. (2019) · IEEE Access · doi:10.1109/access.2018.2887201 · Cited by 110 · see citing works
Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., Aussó, S., Alberich, L. C., Marias, K., Tsiknakis, M., et al. (2021) · arXiv · doi:10.48550/arxiv.2109.09658 · Cited by 58 · see citing works
Blockchain-assisted access for federated Smart Grid domains: Coupling and features
Alcaraz, C., Rubio, J. E., & Lopez, J. (2020) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2020.05.012 · Cited by 38 · see citing works
Managing Provenance Data in Knowledge Graph Management Platforms
Kleinsteuber, E., Al Mustafa, T., Zander, F., König-Ries, B., & Babalou, S. (2024) · Datenbank-Spektrum · doi:10.1007/s13222-023-00463-0 · Cited by 8 · see citing works
Sangaraju, V., & Vissa, S. (2026) · IEEE Access · doi:10.1109/access.2026.3730363 · Cited by 1 · see citing works
“The W3C PROV standard [16] and Herschel et al. [17] model data provenance for extract-transform-load (ETL) pipelines, and LINEAGEX [18] extracts column-level SQL lineage with high accuracy.”
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Kroll, J. A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445937 · Cited by 2 · see citing works
- Authors
- Herschel, M., Diestelkämper, R., & Ben Lahmar, H.
- Affiliations
- University of Stuttgart (DE)
- Corresponding author
- Herschel, Melanie
- ORCID
- Herschel, Melanie
- Venue
- The VLDB Journal
- Published
- 2017-10-16
- Volume
- 26
- Issue
- 6
- Pages
- 881-906
- ISSN
- 0949-877X (ISSN-L)
- 1066-8888
- OpenAlex
- W2766945679
- Cited by
- 300
- References
- 200
- Citations by year
- 2026: 40 · 2025: 34 · 2024: 28 · 2023: 28 · 2022: 37 · 2021: 43 · 2020: 34 · 2019: 34 · 2018: 22
- FWCI
- 39.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The VLDB Journal (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (SFB-TRR 161)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- provenance, data provenance, provenance capture, accountability, reproducibility, privacy, scalability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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