1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. 78th EAGE Conference and Exhibition 2016
  5. Conference paper

Abstract

Summary

Keeping track of the history of subsurface data and how they were generated is a challenge that the industry is facing. We intend to address this challenge through the notion of data provenance, considering seismic interpretation as a case study. We build and extend on the W3C PROV-DM to capture the history of multi-user interpretations and their quality. Our extension included the introduction of new namespaces, named geo and interpret, to accommodate new qualified names for the concepts presented by the model. This provenance model was implemented as a fine-grained relational database model and integrated into a data-centric visualization architecture. A prototype application allowed our users to visualize provenance information on each point by hovering above a horizon feature.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201601157
2016-05-30
2024-04-23

  • From This Site

    /content/papers/10.3997/2214-4609.201601157
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Agrawal, R., Imran, A., Seay, C. and Walker, J.
    [2014] A layer based architecture for provenance in big data. 2014 IEEE International Conference on Big Data.
     [Google Scholar]
  2. Al-Naser, A.
    [2015] Provenance of Visual Interpretations in the Exploration of Data. The University of Manchester, PhD thesis.
     [Google Scholar]
  3. Al-Naser, A., Rasheed, M., Irving, D. and Brooke, J.
    [2013] A Data Centric Approach to Data Provenance in Seismic Imaging Data. 75th EAGE Conference & Exhibition, Extended Abstract, Th 16 03.
     [Google Scholar]
  4. [2014] Fine-Grained Provenance of Users Interpretations in a Collaborative Visualization Architecture. The 5th International Conference on Information Visualization Theory and Applications, 305–317.
     [Google Scholar]
  5. Bavoil, L., Callahan, S., Crossno, P., Freire, J., Scheidegger, C., Silva, C. and Vo, H.
    [2005] VisTrails: Enabling Interactive Multiple-View Visualizations. IEEE VIS 2005, 135–142.
     [Google Scholar]
  6. Belhajjame, K., B’Far, R., Cheney, J., Coppens, S., Cresswell, S., Gil, Y., Groth, P., Klyne, G., Lebo, T., McCusker, J., Miles, S., Myers, J., Sahoo, S. and Tilmes, C.
    [2013] PROV-DM: The PROV Data Model. W3C Recommendation, Moreau, L. and Missier, P., World Wide Web Consortium (W3C).
     [Google Scholar]
  7. Goble, C.
    [2002] Position statement: Musings on provenance, workflow and (semantic web) annotations for bioinformatics. Workshop on Data Derivation and Provenance, Chicago.
     [Google Scholar]
  8. Moreau, L.
    [2010] The Foundations for Provenance on the Web. Foundations and Trends in Web Science, 2, 99–241.
     [Google Scholar]
  9. Silva, C., Freire, J. and Callahan, S.
    [2007] Provenance for Visualizations. Reproducibility and Beyond. Computing in Science Engineering, 9(5), 82–89.
     [Google Scholar]
  10. Simmhan, Y.L., Plale, B. and Gannon, D.
    [2005] A survey of data provenance in science. SIGMOD Rec., 34, 31–36.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201601157
Loading
Provenance of Subsurface Data
Conference Proceedings 2016, 1 (2016); https://doi.org/10.3997/2214-4609.201601157
/content/papers/10.3997/2214-4609.201601157
/content/papers/10.3997/2214-4609.201601157
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error