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VizDSL: A Visual DSL for Interactive Information Visualization

  • Rebecca Morgan
  • Georg Grossmann
  • Michael Schrefl
  • Markus Stumptner
  • Timothy Payne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10816)

Abstract

The development of systems of systems or the replacement of processes or systems can create unknowns, risks, delays and costs which are difficult to understand and characterise, and which frequently result in unforeseen issues resulting in overspend or avoidance. Yet maintaining state of the art processes and systems and utilising best of breed component systems is essential. Visualization of disparate data, systems, processes and standards can help end users to understand relationships such as class hierarchy or communication across system components better. There are many visualization tools and libraries available but they are either a black box when it comes to specifying possible interactions between end users and the visualization or require significant programming skills and manual effort to implement. In this paper we propose a visual language called VizDSL that is based on the Interaction Flow Modeling Language (IFML) for creating highly interactive visualizations. VizDSL can be used to model, share and implement interactive visualization based on model-driven engineering principles. The language has been evaluated based on interaction patterns for visualizations.

Keywords

Model-driven visualization Domain-specific modelling Interactive information visualization IFML 

References

  1. 1.
    Grossmann, G., Igamberdiev, M., Stumptner, M.: Benefits and challenges of multi-level modelling for ecosystem interoperability. In: Proceedings of BDI4E Workshop at I-ESA (2016)Google Scholar
  2. 2.
    Grossmann, G., Jordan, A., Muruganandha, R., Selway, M., Stumptner, M.: Enabling information interoperability through multi-domain modeling. In: Harmsen, F., Proper, H.A. (eds.) PRET 2013. LNBIP, vol. 151, pp. 16–33. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38774-6_2CrossRefGoogle Scholar
  3. 3.
    Selway, M., Stumptner, M., Mayer, W., Jordan, A., Grossmann, G., Schrefl, M.: A conceptual framework for large-scale ecosystem interoperability and industrial product lifecycles. Data Knowl. Eng. 109, 85–111 (2017)CrossRefGoogle Scholar
  4. 4.
    Morgan, R., Grossmann, G., Stumptner, M.: VizDSL: towards a graphical visualisation language for enterprise systems interoperability. In: Proceedings of Symposium on Big Data Visual Analytics (BDVA). IEEE (2017)Google Scholar
  5. 5.
    Heer, J., Agrawala, M.: Software design patterns for information visualization. IEEE Trans. Visual Comput. Graph. 12(5), 853–860 (2006)CrossRefGoogle Scholar
  6. 6.
    Brambilla, M., Cabot, J., Wimmer, M.: Model-Driven Software Engineering in Practice, 2nd edn. Morgan & Claypool Publishers, San Rafael (2017)Google Scholar
  7. 7.
    Jones, C., Jia, X.: Using a domain specific language for lightweight model-driven development. In: Maciaszek, L.A., Filipe, J. (eds.) ENASE 2014. CCIS, vol. 551, pp. 46–62. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27218-4_4CrossRefGoogle Scholar
  8. 8.
    Schmidt, D.C.: Guest editor’s introduction: model-driven engineering. Computer 39, 25–31 (2006)CrossRefGoogle Scholar
  9. 9.
    Fill, H.G.: Visualisation for Semantic Information Systems, 1st edn. Gabler Verlag, Wiesbaden (2009)CrossRefGoogle Scholar
  10. 10.
    Howse, J., Stapleton, G., Taylor, K., Chapman, P.: Visualizing ontologies: a case study. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 257–272. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25073-6_17CrossRefGoogle Scholar
  11. 11.
    Kocbek, S., Kim, J.D., Perret, J.L., Whetzel, P.L.: Visualizing ontology mappings to help ontology engineers identify relevant ontologies for their reuse. In: Proceedings of 4th International Conference on Biomedical Ontology (2013)Google Scholar
  12. 12.
    Burgstaller, F., Stabauer, M., Morgan, R., Grossmann, G.: Towards customised visualisation of ontologies. In: Proceedings of the Australasian Computer Science Week Multiconference (ACSW), pp. 1–10. ACM Press (2017)Google Scholar
  13. 13.
    Moody, D.: The physics of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng. 35(6), 756–779 (2009)CrossRefGoogle Scholar
  14. 14.
    Aranda-Corral, G.A., Borrego-Diaz, J., Chavez-Gonzalez, A.M.: Repairing conceptual relations in ontologies by means of an interactive visual reasoning: cognitive and design principles. In: Proceedings of the 3rd IEEE International Conference on Cognitive Infocommunications (CogInfoCom), pp. 739–744. IEEE (2012)Google Scholar
  15. 15.
    Voigt, M., Pietschmann, S., Meißner, K.: Semantic Models for Adaptive Interactive Systems. Human-Computer Interaction, pp. 1–25 (2013)Google Scholar
  16. 16.
    Nazemi, K., Burkhardt, D., Ginters, E., Kohlhammer, J.: Semantics visualization - definition, approaches and challenges. Procedia Comput. Sci. 75, 75–83 (2015)CrossRefGoogle Scholar
  17. 17.
    Bull, R.I., Favre, J.M.: Visualization in the context of model driven engineering. In: MDDAUI (2005)Google Scholar
  18. 18.
    Bull, R.I., Storey, M.A., Favre, J.M., Litoiu, M.: An architecture to support model driven software visualization. In: Proceedings of the 14th IEEE International Conference on Program Comprehension (ICPC), pp. 100–106. IEEE (2006)Google Scholar
  19. 19.
    Bull, R.I.: Model driven visualization: towards a model driven engineering approach for information visualization. Ph.D. thesis (2008)Google Scholar
  20. 20.
    Ren, L., Tian, F., Zhang, X., Zhang, L.: DaisyViz: a model-based user interface toolkit for interactive information visualization systems. Visual Lang. Comput. 21(4), 209–229 (2010)CrossRefGoogle Scholar
  21. 21.
    Weerasiri, D., Barukh, M.C., Benatallah, B., Jian, C.: CloudMap: a visual notation for representing and managing cloud resources. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 427–443. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39696-5_26CrossRefGoogle Scholar
  22. 22.
    Cabanillas, C., Knuplesch, D., Resinas, M., Reichert, M., Mendling, J., Ruiz-Cortés, A.: RALph: a graphical notation for resource assignments in business processes. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 53–68. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19069-3_4CrossRefGoogle Scholar
  23. 23.
    Sendall, S., Kozaczynski, W.: Model transformation: the heart and soul of model-driven software development. IEEE Softw. 20(5), 42–45 (2003)CrossRefGoogle Scholar
  24. 24.
    Brambilla, M., Fraternali, P.: Interaction Flow Modeling Language: Model-Driven UI Engineering of Web and Mobile Apps with IFML, 1st edn. Morgan Kaufmann, San Francisco (2015)Google Scholar
  25. 25.
    Giraldo, F.D., Espana, S., Giraldo, W.J., Pastor, O.: Modelling language quality evaluation in model-driven information systems engineering: a roadmap. In: Proceedings of 9th IEEE Conference on Research Challenges in Information Science (RCIS), pp. 64–69 (2015)Google Scholar
  26. 26.
    Condori-Fernandez, N., Panach, J.I., Baars, A.I., Vos, T., Pastor, O.: An empirical approach for evaluating the usability of model-driven tools. Sci. Comput. Program. 78(11), 2245–2258 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rebecca Morgan
    • 1
  • Georg Grossmann
    • 1
  • Michael Schrefl
    • 1
  • Markus Stumptner
    • 1
  • Timothy Payne
    • 2
  1. 1.University of South AustraliaAdelaideAustralia
  2. 2.Lockheed Martin STELaRLabEdinburghAustralia

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