User’s Interpretations of Features in Visualization

  • Aqeel Al-Naser
  • Masroor Rasheed
  • Duncan Irving
  • John Brooke
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 550)


Visualization is often used to identify features of interest in a dataset. The identification of features cannot be fully automated and the subjective interpretation of the user is involved in the identification of the feature. There can be many such interpretations, both from a single user as s/he explores the data, and also in collaborations. Managing all these interpretations is problematic. We propose a novel visualization architecture that addresses this problem. We illustrate our method by examining how geoseismic data is interpreted, since this application presents all of the issues above.


Geospatial visualization Data acquisition and management Provenance Data exploration Query-driven visualization 


  1. 1.
    Robein, E.: Seismic Imaging: A Review of the Techniques, their Principles, Merits and Limitations. EAGE Publications bv, aaa (2010)Google Scholar
  2. 2.
    Plate, J., Tirtasana, M., Carmona, R., Fröhlich, B.: Octreemizer: a hierarchical approach for interactive roaming through very large volumes. In: Data Visualisation, pp. 53–60. Eurographics Association (2002)Google Scholar
  3. 3.
    Castanie, L., Levy, B., Bosquet, F.: VolumeExplorer: roaming large volumes to couple visualization and data processing for oil and gas exploration. In: IEEE Visualization, vol. im, pp. 247–254. IEEE (2005)Google Scholar
  4. 4.
    Lin, J.C.R., Hall, C.: Multiple oil and gas volumetric data visualization with GPU programming. In: Proceedings of SPIE 6495, pp. 64950U–64950U-8 (2007)Google Scholar
  5. 5.
    Plate, J., Holtkaemper, T., Froehlich, B.: A flexible multi-volume shader framework for arbitrarily intersecting multi-resolution datasets. IEEE Trans. Vis. Comput. Graph. 13, 1584–1591 (2007)CrossRefGoogle Scholar
  6. 6.
    Patel, D., Sture, O.Y., Hauser, H., Giertsen, C., EduardGröller, M.: Knowledge-assistedvisualization of seismic data. Comput. Graph. 33, 585–596 (2009)Google Scholar
  7. 7.
    Patel, D., Bruckner, S., Viola, I.: Seismic volume visualization for horizon extraction. In: Proceedings of IEEE Pacific Visualization, vol. Vi, pp. 73–80. IEEE, Taipei (2010)Google Scholar
  8. 8.
    Höllt, T., Beyer, J., Gschwantner, F., Muigg, P., Doleisch, H., Heinemann, G., Hadwiger, M.: Interactive seismic interpretation with piecewise global energy minimization. In: 2011 IEEE Pacific Visualization Symposium (PacificVis), Hong Kong, pp. 59–66 (2011)Google Scholar
  9. 9.
    Visualization Sciences Group: Avizo Earth (2013).
  10. 10.
    Halliburton-Landmark: GeoProbe Volume Visualization (2013).
  11. 11.
    Schlumberger: Petrel Seismic to Simulation Software (2013).
  12. 12.
    Bacon, M., Simm, R., Redshaw, T.: 3-D Seismic Interpretation. Cambridge University Press, Cambridge (2003)CrossRefGoogle Scholar
  13. 13.
    Al-Naser, A., Rasheed, M., Irving, D., Brooke, J.: A data centric approach to data provenance in seismic imaging data. In: 75th EAGE Conference & Exhibition incorporating SPE EUROPEC. EAGE Publications bv, London (2013)Google Scholar
  14. 14.
    Al-Naser, A., Rasheed, M., Irving, D., Brooke, J.: A visualization architecture for collaborative analytical and data provenance activities. In: 2013 17th International Conference on Information Visualisation (IV), pp. 253–262 (2013)Google Scholar
  15. 15.
    Moreland, K.: A survey of visualization pipelines. IEEE Trans. Vis. Comput. Graph. 19, 367–378 (2013)CrossRefGoogle Scholar
  16. 16.
    Ahrens, J., Brislawn, K., Martin, K., Geveci, B., Law, C., Papka, M.: Large-scale data visualization using parallel data streaming. IEEE Comput. Graph. Appl. 21, 34–41 (2001)CrossRefGoogle Scholar
  17. 17.
    Biddiscombe, J., Geveci, B., Martin, K., Moreland, K., Thompson, D.: Time dependent processing in a parallel pipeline architecture. IEEE Trans, Vis. Comput. Graph. 13, 1376–1383 (2007)CrossRefGoogle Scholar
  18. 18.
    Stockinger, K., Shalf, J., Wu, K., Bethel, E.: Query-driven visualization of large data sets. In: IEEE Visualization, VIS 2005, pp. 167–174. IEEE (2005)Google Scholar
  19. 19.
    Gosink, L.J., Anderson, J.C., Bethel, E.W., Joy, K.I.: Query-driven visualization of time-varying adaptive mesh refinement data. IEEE Trans. Vis. Comput. Graph. 14, 1715–1722 (2008)CrossRefGoogle Scholar
  20. 20.
    Wu, K.: FastBit: an efficient indexing technology for accelerating data-intensive science. J. Phys. Conf. Ser. 16, 556–560 (2005)CrossRefGoogle Scholar
  21. 21.
    Wu, K., Ahern, S., Bethel, E.W., Chen, J., Childs, H., Cormier-Michel, E., Geddes, C., Gu, J., Hagen, H., Hamann, B., Koegler, W., Lauret, J., Meredith, J., Messmer, P., Otoo, E., Perevoztchikov, V., Poskanzer, A., Prabhat, Rübel, O., Shoshani, A., Sim, A., Stockinger, K., Weber, G., Zhang, W.M.: FastBit: interactively searching massive data. J. Phys. Conf. Ser. 180, 012053 (2009)Google Scholar
  22. 22.
    Vo, H., Bronson, J., Summa, B., Comba, J., Freire, J., Howe, B., Pascucci, V., Silva, C.: Parallel visualization on large clusters using map reduce. In: 2011 IEEE Symposium on Large Data Analysis and Visualization (LDAV), pp. 81–88. IEEE (2011)Google Scholar
  23. 23.
    Pavlo, A., Paulson, E., Rasin, A., Abadi, D.J., DeWitt, D.J., Madden, S., Stonebraker, M.: A comparison of approaches to large-scale data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 165–178. ACM (2009)Google Scholar
  24. 24.
    Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-science. SIGMOD Rec. 34, 31–36 (2005)CrossRefGoogle Scholar
  25. 25.
    Ikeda, R., Widom, J.: Data lineage: a survey. Technical report, Stanford University (2009)Google Scholar
  26. 26.
    Bavoil, L., Callahan, S., Crossno, P., Freire, J., Scheidegger, C., Silva, C., Vo, H.: VisTrails: enabling interactive multiple-view visualizations. In: IEEE Visualization, VIS 2005, pp. 135–142. IEEE (2005)Google Scholar
  27. 27.
    Scheidegger, C.E., Vo, H., Koop, D., Freire, J., Silva, C.T.: Querying and creating visualizations by analogy. IEEE Trans. Vis. Comput. Graph. 13, 1560–1567 (2007)CrossRefGoogle Scholar
  28. 28.
    Hawtin, S., Lecore, D.: The business value case for data management - a study. Technical report, CDA & Schlumberger (2011)Google Scholar
  29. 29.
    Jomier, J., Aylward, S.R., Marion, C., Lee, J., Styner, M.: A digital archiving system and distributed server-side processing of large datasets. In: Siddiqui, K.M., Liu, B.J. (eds.) Proceedings of SPIE 7264, Medical Imaging 2009: Advanced PACS-based Imaging Informatics and Therapeutic Applications, vol. 7264, pp. 726413–726413-8 (2009)Google Scholar
  30. 30.
    Jomier, J., Jourdain, S., Marion, C.: Remote visualization of large datasets with MIDAS and ParaViewWeb. In: Proceedings of the 16th International Conference on 3D Web Technology, Paris, France, pp. 147–150. ACM (2011)Google Scholar
  31. 31.
    Alvarez, F., Dineen, P., Nimbalkar, M.: The Studio Environment: Driving Productivity for the E&P Workforce. White paper, Schlumberger (2013)Google Scholar
  32. 32.
    Ma, C., Rokne, J.: 3D seismic volume visualization. In: Zhang, D.D., Kamel, M., Baciu, G. (eds.) Integrated Image and Graphics Technologies, vol. 762, pp. 241–262. Springer, Netherlands (2004)CrossRefGoogle Scholar
  33. 33.
    BP: BP statistical review of world energy June 2012. Technical report (2012)Google Scholar
  34. 34.
    BP: BP energy outlook 2030. Technical report, London (2012)Google Scholar
  35. 35.
    Society of exploration geophysicists: SEG Y rev 1 data exchange format. Technical Report, May 2002Google Scholar
  36. 36.
    Al-Naser, A., Rasheed, M., Brooke, J., Irving, D.: Enabling visualization of massive datasets through MPP database architecture. In: Carr, H., Grimstead, I. (eds.) Theory and Practice of Computer Graphics, pp. 109–112. Eurographics Association (2011)Google Scholar
  37. 37.
    Brooke, J.M., Marsh, J., Pettifer, S., Sastry, L.S.: The importance of locality in the visualization of large datasets. Concurrency Comput. Pract. Experience 19, 195–205 (2007)CrossRefGoogle Scholar
  38. 38.
    Zhang, S., Zhao, J.: Feature aware multiresolution animation models generation. J. Multimedia 5, 622–628 (2010)Google Scholar
  39. 39.
    Mcreynolds, T., Hui, S.: Volume visualization with texture. In: SIGGRAPH, pp. 144–153 (1997)Google Scholar
  40. 40.
    Hadwiger, M., Ljung, P., Salama, C.R., Ropinski, T.: Advanced illumination techniques for GPU volume raycasting. In: ACM SIGGRAPH Courses Program, pp. 1–166. ACM, New York (2009)Google Scholar
  41. 41.
    Karvounarakis, G., Ives, Z.G., Tannen, V.: Querying data provenance. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, pp. 951–962. ACM, New York (2010)Google Scholar
  42. 42.
    Rahimi, S.K., Haug, F.S.: Query Optimization. Wiley, New York (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Aqeel Al-Naser
    • 1
  • Masroor Rasheed
    • 2
  • Duncan Irving
    • 2
  • John Brooke
    • 1
  1. 1.School of Computer ScienceThe University of ManchesterManchesterUK
  2. 2.Teradata CorporationLondonUK

Personalised recommendations