Skip to main content

Advertisement

SpringerLink
Log in

Cross-table linking and brushing: interactive visual analysis of multiple tabular data sets

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Studying complex problems often requires identifying and exploring connections and dependencies among several, seemingly unrelated, data sets. Those data sets are often represented as data tables. We propose a novel approach to studying such data sets using linking and brushing across multiple data tables in a coordinated multiple views system. We first identify possible mappings from a subset of one data set to a subset of another data set. That collection of mappings is then used to specify linking among data sets and to support brushing across data sets. Brushing in one data set is then mapped to a brush in the destination data set. If the brush is refined in the destination data set, the inverse mapping, or a back-link, is used to determine the refined brush in the original data set. Brushing and back-links make it possible to efficiently create and analyze complex queries interactively in an iterative process. That process is further supported by a user interface that keeps track of the mappings, links and brushes. The proposed approach is evaluated using three data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Becker, R.A., Cleveland, W.S.: Brushing scatterplots. Technometrics 29(2), 127–142 (1987)

    Article  MathSciNet  Google Scholar 

  2. Card, S.K., Mackinlay, J., Shneiderman, B. (eds.): Readings in Information Visualization: Using Vision to Think. Interactive Technologies. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  3. Cavalcanti, V.M.B., Schiel, U., de Souza Baptista, C.: Querying spatio-temporal databases using a visual environment. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 412–419. ACM, New York (2006)

  4. Cerullo, C., Porta, M.: A system for database visual querying and query visualization: Complementing text and graphics to increase expressiveness. In: Proceedings of the 18th International Workshop on Database and Expert Systems Applications (DEXA 2007), pp. 109–113 (2007)

  5. Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970)

    Article  MATH  Google Scholar 

  6. Date, C.J., Darwen, H.: A Guide to SQL Standard, 4th edn. Addison-Wesley Professional, Boston (1997)

    Google Scholar 

  7. Della Penna, G., Magazzeni, D., Orefice, S.: A general theory of spatial relations to support a graphical tool for visual information extraction. J. Vis. Lang. Comput. 24(2), 71–87 (2013)

    Article  Google Scholar 

  8. Doleisch, H., Gasser, M., Hauser, H.: Interactive feature specification for focus+context visualization of complex simulation data. In: G.P. Bonneau, S. Hahmann, C.D. Hansen (eds.) Proceedings of the Joint EUROGRAPHICS—IEEE TCVG Symposium on Visualization, pp. 239–248. The Eurographics Association (2003)

  9. Fisherkeller, M.A., Friedman, J.H., Tukey, J.W.: PRIM-9: an interactive multidimensional data display and analysis system (1974). In: W.S. Cleveland, M.E. McGill (eds.) Dynamic Graphics for Statistics, chap. 3, pp. 91–110. CRC Press (1988)

  10. Ganuza, M.L., Ferracutti, G., Gargiulo, M.F., Castro, S.M., Bjerg, E., Gröller, E., Matković, K.: The spinel explorer—interactive visual analysis of spinel group minerals. IEEE Trans. Vis. Comput. Graph. 20(12), 1913–1922 (2014)

    Article  Google Scholar 

  11. Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems: The Complete Book, second edn, p. 07458. Prentice Hall, Upper Saddle River (2009)

    Google Scholar 

  12. Hauser, H., Ledermann, F., Doleisch, H.: Angular brushing of extended parallel coordinates. In: Proceedings of the 2002 IEEE Symposium on Information Visualization (INFOVIS 2002), pp. 127–130 (2002)

  13. Konyha, Z., Matković, K., Gračanin, D., Jelović, M., Hauser, H.: Interactive visual analysis of families of function graphs. IEEE Trans. Vis. Comput. Graph. 12(6), 1373–1385 (2006)

    Article  Google Scholar 

  14. Kosara, R., Hauser, H., Gresh, D.L.: An interaction view on information visualization. In: Proceedings of the Eurographics State-of-the-Art 2003 (EG 2003), pp. 123–137 (2003)

  15. Lichman, M.: UCI machine learning repository. http://archive.ics.uci.edu/ml. University of California, Irvine, School of Information and Computer Sciences (2013). Accessed 21 Jan 2018

  16. Lin, S., Fortuna, J., Kulkarni, C., Stone, M., Heer, J.: Selecting semantically-resonant colors for data visualization. In: Proceedings of the 15th Eurographics Conference on Visualization, pp. 401–410. The Eurographics Association, Chichester, UK (2013)

  17. Liu, Z., Navathe, S.B., Stasko, J.T.: Network-based visual analysis of tabular data. In: Proceedings of the 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 41–50 (2011)

  18. Mahalanobis, P.C.: On the generalised distance in statistics. Proc. Natl. Inst. Sci. India II(1), 49–55 (1936)

    MATH  Google Scholar 

  19. Martin, A.R., Ward, M.O.: High dimensional brushing for interactive exploration of multivariate data. In: Proceedings of the IEEE Conference on Visualization (Visualization’95), pp. 271–278 (1995)

  20. Matkovic, K., Freiler, W., Gracanin, D., Hauser, H.: Comvis: a coordinated multiple views system for prototyping new visualization technology. In: Information Visualisation, 2008. IV’08. 12th International Conference, pp. 215–220. IEEE (2008)

  21. McLachlan, G.J.: Mahalanobis distance. Resonance 4(6), 20–26 (1999)

    Article  Google Scholar 

  22. NOAA: land-based datasets and products. http://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets. National Centers for Environmental Information (2018). Accessed 21 Jan 2018

  23. North, C., Shneiderman, B.: Snap-together visualization: a user interface for coordinating visualizations via relational schemata. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 128–135. ACM, New York (2000)

  24. Pace, R.K., Barry, R.: Sparse spatial autoregressions. Stat. Probab. Lett. 33(3), 291–297 (1997)

    Article  MATH  Google Scholar 

  25. Radoš, S., Splechtna, R., Matković, K., Đuras, M., Gröller, E., Hauser, H.: Towards quantitative visual analytics with structured brushing and linked statistics. Comput. Graph. Forum 35(3), 251–260 (2016)

    Article  Google Scholar 

  26. Shadoan, R., Weaver, C.: Visual analysis of higher-order conjunctive relationships in multidimensional data using a hypergraph query system. IEEE Trans. Vis. Comput. Graph. 19(12), 2070–2079 (2013)

    Article  Google Scholar 

  27. Splechtna, R., Matković, K., Gračanin, D., Jelović, M., Hauser, H.: Interactive visual steering of hierarchical simulation ensembles. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2015), pp. 89–96 (2015)

  28. Turkay, C., Filzmoser, P., Hauser, H.: Brushing dimensions—a dual visual analysis model for high-dimensional data. IEEE Trans. Vis. Comput. Graph. 17(12), 2591–2599 (2011)

    Article  Google Scholar 

  29. Vincenty, T.: Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Surv. Rev. 23(176), 88–93 (1975)

    Article  Google Scholar 

  30. Weaver, C.: Building highly-coordinated visualizations in improvise. In: Proceedings of the 2004 IEEE Symposium on Information Visualization, pp. 159–166 (2004)

  31. Weaver, C.: Cross-filtered views for multidimensional visual analysis. IEEE Trans. Vis. Comput. Graph. 16(2), 192–204 (2010)

    Article  Google Scholar 

Download references

Funding

This study was funded by BMVIT, BMWFW, Styria, SFG and Vienna Business Agency in the scope of COMET (854174).

Author information

Authors and Affiliations

  1. VRVis Research Center, Donau-City Str. 11, 1220, Vienna, Austria

    Rainer Splechtna, Michael Beham, Katja Bühler & Krešimir Matković

  2. Virginia Tech, Blacksburg, VA, 24060, USA

    Denis Gračanin

  3. VyGLab Research Laboratory, DCIC, UNS, San Andrés 800, 8000, Bahía Blanca, Argentina

    María Luján Ganuza

  4. University of Zagreb, Unska 3, 10000, Zagreb, Croatia

    Igor Sunday Pandžić

Authors
  1. Rainer Splechtna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Beham
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Denis Gračanin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. María Luján Ganuza
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Katja Bühler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Igor Sunday Pandžić
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Krešimir Matković
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to María Luján Ganuza.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (wmv 33514 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Splechtna, R., Beham, M., Gračanin, D. et al. Cross-table linking and brushing: interactive visual analysis of multiple tabular data sets. Vis Comput 34, 1087–1098 (2018). https://doi.org/10.1007/s00371-018-1516-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-018-1516-8

Keywords

Search

Navigation