KNOVA: INTRODUCING A REFERENCE MODEL FOR KNOWLEDGE-BASED VISUAL ANALYTICS

Stefan Flöring, H.-Jürgen Appelrath

Abstract

When creating interactive applications for data exploration three major challenges can be identified: The integration of heterogeneous data sources at runtime, the integration of suitable visualization methods and the availability of interaction methods which enable domain experts to (implicitly) apply their expert knowledge in the knowledge driven exploration process. To address these challenges we introduce the KnoVA (Knowledge-Based Visual Analytics) reference model, which allows for generating a description of visualization methods, interaction methods and data sources. We then outline how this model can be useful to create knowledge based visual analytics systems in a model driven software development process.

References

  1. Aigner, W., Bertone, A., Miksch, S., Tominski, C., and Schumann, H. (2007). Towards a conceptual framework for visual analytics of time and time-oriented data. In WSC 7807: Proceedings of the 39th conference on Winter simulation, pages 721-729, Piscataway, NJ, USA. IEEE Press.
  2. Brunk, C., Kelly, J., and Kohavi, R. (1997). Mineset: An integrated system for data mining. In KDD, pages 135- 138.
  3. Chi, E. H. (2002). Expressiveness of the data flow and data state models in visualization systems. In AVI 7802: Proceedings of the Working Conference on Advanced Visual Interfaces, pages 375-378, New York, NY, USA. ACM.
  4. Chuah, M. C., Roth, S. F., Mattis, J., and Kolojejchick, J. (1995). Sdm: Selective dynamic manipulation of visualizations. In ACM Symposium on User Interface Software and Technology, pages 61-70.
  5. Eick, S. G. (2009). Data visualization software - advizor solutions. ”'Website”78.
  6. Elmqvist, N., Stasko, J., and Tsigas, P. (2008). Datameadow: a visual canvas for analysis of largescale multivariate data. Information Visualization, 7(1):18-33.
  7. Flöring, S. and Hesselmann, T. (2010). Tap: Towards visual analytics on interactive surfaces. In Collaborative Visualization on Interactive Surfaces - CoVIS 7809, number 2010-2, pages 9-12, Munich, Germany. LMU Media Informatics. Technical Report.
  8. Haber, R. and McNabb, D. A. (1990). Visualization idioms: A conceptual model for scientific visualization systems. In Visualization in Scientific Computing.
  9. Hinneburg, A., Keim, D. A., and Wawryniuk, M. (2003). Hd-eye - visual clustering of high dimensional data: a demonstration. IEEE Computer Graphics and Applications, 19(5):735-755.
  10. Keim, D. A. (2001). Visual exploration of large data sets. Commun. ACM, 44(8):38-44.
  11. Keim, D. A., Mansmann, F., Stoffel, A., and Ziegler, H. (2009). Visual analytics. In Encyclopedia of Database Systems. Springer.
  12. Levendovszky, T., Lengyel, L., Mezei, G., and Charaf, H. (2005). A systematic approach to metamodeling environments and model transformation systems in vmts. In Electronic Notes in Theoretical Computer Science, pages 65-75.
  13. Liu, Z., Stasko, J., and Sullivan, T. (2009). Selltrend: Inter-attribute visual analysis of temporal transaction data. IEEE Transactions on Visualization and Computer Graphics, 15(6):1025-1032.
  14. Meister, J., Rohde, M., Appelrath, H.-J., and Kamp, V. (2003). Data-warehousing im gesundheitswesen. it - Information Technology, 45(4):179-185.
  15. Pfitzner, D., Hobbs, V., and Powers, D. M. W. (2003). A unified taxonomic framework for information visualization. In Pattison, T. and Thomas, B. H., editors, InVis.au, volume 24 of CRPIT, pages 57-66. Australian Computer Society.
  16. Tang, D., Stolte, C., and Bosch, R. (2004). Design choices when architecting visualizations. Information Visualization, 3(2):65-79.
  17. Tobias, M., Isenberg, P., and Carpendale, S. (2009). Lark: Coordinating co-located collaboration with information visualization. IEEE Transactions on Visualization and Computer Graphics, 15(6):1065-1072.
  18. Valiati, E. R. A., Pimenta, M. S., and Freitas, C. M. D. S. (2006). A taxonomy of tasks for guiding the evaluation of multidimensional visualizations. In BELIV 7806: Proceedings of the 2006 AVI workshop on BEyond time and errors, pages 1-6, New York, NY, USA. ACM.
  19. Wehrend, S. and Lewis, C. (1990). A problem-oriented classification of visualization techniques. In VIS 7890: Proceedings of the 1st conference on Visualization 7890, pages 139-143, Los Alamitos, CA, USA. IEEE Computer Society Press.
  20. Wenzel, S., Bernhard, J., and Jessen, U. (2003). Visualization for modeling and simulation: a taxonomy of visualization techniques for simulation in production and logistics. In Chick, S. E., Sanchez, P. J., Ferrin, D. M., and Morrice, D. J., editors, Winter Simulation Conference, pages 729-736. ACM.
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Paper Citation


in Harvard Style

Flöring S. and Appelrath H. (2011). KNOVA: INTRODUCING A REFERENCE MODEL FOR KNOWLEDGE-BASED VISUAL ANALYTICS . In Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2011) ISBN 978-989-8425-46-1, pages 230-235. DOI: 10.5220/0003325402300235


in Bibtex Style

@conference{ivapp11,
author={Stefan Flöring and H.-Jürgen Appelrath},
title={KNOVA: INTRODUCING A REFERENCE MODEL FOR KNOWLEDGE-BASED VISUAL ANALYTICS},
booktitle={Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2011)},
year={2011},
pages={230-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003325402300235},
isbn={978-989-8425-46-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2011)
TI - KNOVA: INTRODUCING A REFERENCE MODEL FOR KNOWLEDGE-BASED VISUAL ANALYTICS
SN - 978-989-8425-46-1
AU - Flöring S.
AU - Appelrath H.
PY - 2011
SP - 230
EP - 235
DO - 10.5220/0003325402300235