Towards Scalable Visual Exploration of Very Large RDF Graphs

  • Nikos Bikakis
  • John Liagouris
  • Maria Kromida
  • George Papastefanatos
  • Timos Sellis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9341)

Abstract

In this paper, we outline our work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large graphs. The proposed platform, called graphVizdb, is based on a novel technique for indexing and storing the graph. Particularly, the graph layout is indexed with a spatial data structure, i.e., an R-tree, and stored in a database. In runtime, user operations are translated into efficient spatial operations (i.e., window queries) in the backend.

Keywords

graphVizdb Graph data Disk based visualization tool RDF graph visualization Spatial Visualizing linked data Partition based graph layout 

References

  1. 1.
    Abello, J., van Ham, F., Krishnan, N.: ASK-GraphView: a large scale graph visualization system. IEEE Trans. Vis. Comput. Graph. 12(5), 669–676 (2006)CrossRefGoogle Scholar
  2. 2.
    Alonen, M., Kauppinen, T., Suominen, O., Hyvönen, E.: Exploring the linked university data with visualization tools. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 204–208. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  3. 3.
    Archambault, D., Munzner, T., Auber, D.: GrouseFlocks: steerable exploration of graph hierarchy space. IEEE Trans. Vis. Comput. Graph. 14(4), 900–913 (2008)CrossRefGoogle Scholar
  4. 4.
    Auber, D.: Tulip - a huge graph visualization framework. In: Jünger, M., Mutzel, P. (eds.) Graph Drawing Software, pp. 105–126. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: ICWSM (2009)Google Scholar
  6. 6.
    Benedetti, F., Po, L., Bergamaschi, S.: A visual summary for linked open data sources. In: ISWC (2014)Google Scholar
  7. 7.
    Bikakis, N., Skourla, M., Papastefanatos, G.: rdf:SynopsViz – a framework for hierarchical linked data visual exploration and analysis. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC Satellite Events 2014. LNCS, vol. 8798, pp. 292–297. Springer, Heidelberg (2014) Google Scholar
  8. 8.
    Brunetti, J.M., Auer, S., García, R., Klímek, J., Necaský, M.: Formal linked data visualization model. In: IIWAS (2013)Google Scholar
  9. 9.
    Dadzie, A., Rowe, M.: Approaches to visualising linked data: a survey. Semant. Web 2(2), 89–124 (2011)Google Scholar
  10. 10.
    Dudáš, M., Zamazal, O., Svátek, V.: Roadmapping and navigating in the ontology visualization landscape. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS, vol. 8876, pp. 137–152. Springer, Heidelberg (2014) Google Scholar
  11. 11.
    Fu, B., Noy, N.F., Storey, M.-A.: Eye tracking the user experience - an evaluation of ontology visualization techniques. Semant. Web J. (2015)Google Scholar
  12. 12.
    Hastrup, T., Cyganiak, R., Bojars, U.: Browsing linked data with fenfire. In: WWW (2008)Google Scholar
  13. 13.
    Heim, P., Lohmann, S., Stegemann, T.: Interactive relationship discovery via the semantic web. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 303–317. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  14. 14.
    Rodrigues Jr., J.F., Tong, H., Traina, A.J.M., Faloutsos, C., Leskovec, J.: GMine: a system for scalable, interactive graph visualization and mining. In: VLDB (2006)Google Scholar
  15. 15.
    Lin, Z., Cao, N., Tong, H., Wang, F., Kang, U., Chau, D.H.P.: Demonstrating interactive multi-resolution large graph exploration. In: ICDM (2013)Google Scholar
  16. 16.
    Mazumdar, S., Petrelli, D., Ciravegna, F.: Exploring user and system requirements of linked data visualization through a visual dashboard approach. Semant. Web 5(3), 203–220 (2014)Google Scholar
  17. 17.
    Mazumdar, S., Petrelli, D., Elbedweihy, K., Lanfranchi, V., Ciravegna, F.: Affective graphs: the visual appeal of linked data. Semant. Web 6(3), 277–312 (2015)Google Scholar
  18. 18.
    Stuhr, M., Roman, D., Norheim, D.: LODWheel - JavaScript-based visualization of RDF data. In: Workshop on Consuming Linked Data (2011)Google Scholar
  19. 19.
    Tominski, C., Abello, J., Schumann, H.: CGV - an interactive graph visualization system. Comput. Graph. 33(6), 660–678 (2009)CrossRefGoogle Scholar
  20. 20.
    Vocht, L.D., Dimou, A., Breuer, J., Compernolle, M.V., Verborgh, R., Mannens, E., Mechant, P., de Walle, R.V.: A visual exploration workflow as enabler for the exploitation of linked open data. In: IESD (2014)Google Scholar
  21. 21.
    Zhang, K., Wang, H., Tran, D.T., Yu, Y.: ZoomRDF: semantic fisheye zooming on RDF data. In: WWW (2010)Google Scholar
  22. 22.
    Zinsmaier, M., Brandes, U., Deussen, O., Strobelt, H.: Interactive level-of-detail rendering of large graphs. IEEE Trans. Vis. Comput. Graph. 18(12), 2486–2495 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nikos Bikakis
    • 1
    • 2
  • John Liagouris
    • 3
  • Maria Kromida
    • 1
  • George Papastefanatos
    • 2
  • Timos Sellis
    • 4
  1. 1.NTU AthensAthensGreece
  2. 2.ATHENA Research CenterAthensGreece
  3. 3.ETH ZürichZürichSwitzerland
  4. 4.RMIT UniversityMelbourneAustralia

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