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Intelligent Visual Analytics – a Human-Adaptive Approach for Complex and Analytical Tasks

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Intelligent Human Systems Integration (IHSI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 722))

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Abstract

Visual Analytics enables solving complex and analytical tasks by combining automated data analytics methods and interactive visualizations. The complexity of tasks, the huge amount of data and the complex visual representation may overstrain the users of such systems. Intelligent and adaptive visualizations system show already promising results to bridge the gap between human and the complex visualization. We introduce in this paper a revised version of layer-based visual adaptation model that considers the human perception and cognition abilities. The model is then used to enhance the most popular Visual Analytics model to enable the development of Intelligent Visual Analytics systems.

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References

  1. Nazemi, K.: Adaptive Semantics Visualization. Studies in Computational Intelligence, p. 422. Springer Interntational Publishing (2016). ISBN: 978-3-319-30815-9. https://doi.org/10.1007/978-3-319-30816-6

  2. Gotz, D., Lu, Z., When, J., Kissa, P., Cao, N., Qian, W.H., Liu, S.X., Zhou, M.X.: HARVEST: an intelligent visual analytic tool for the masses. In: Proceedings of IVITA 2010, pp. 1–4. ACM, New York (2010)

    Google Scholar 

  3. Ahn, J.-W., Brusilovsky, P.: Adaptive visualization for exploratory information retrieval. Inf. Process. Manage. 49(5), 1139–1164 (2013)

    Article  Google Scholar 

  4. Steichen, B., Carenini, G., Conati, C.: User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities. In: Proceedings of IUI 2013, IUI 2013, pp. 317–328. ACM, NewYork (2013)

    Google Scholar 

  5. Bai, X., White, D., Sundaram, D.: Contextual adaptive knowledge visualization environments. Electron. J. Knowl. Manag. 10(1), 01–14 (2012)

    Google Scholar 

  6. Wiza, W., Walczak, K., Cellary, W.: AVE - method for 3D visualization of search results. In: Web Engineering. Lecture Notes in Computer Science, vol. 2722, pp. 204–207. Springer, Heidelberg (2003)

    Google Scholar 

  7. Brusilovsky, P., Ahn, J.W., Dumitriu, T., Yudelson, M.: Adaptive knowledge-based visualization for accessing educational examples. In: 2006 Tenth International Conference on Information Visualization, IV 2006, pp. 142–150 (2006)

    Google Scholar 

  8. Shi, L., Cao, N., Liu, S., Qian, W., Tan, L., Wang, G., Sun, J., Lin, C.-Y.: HiMAP: adaptive visualization of large-scale online social networks. In: 2009 IEEE Pacific Visualization Symposium, PacificVis 2009, pp. 41–48 (2009)

    Google Scholar 

  9. Gotz, D., Zhou, M.X.: An empirical study of user interaction behavior during visual analysis. Technical report, IBM Research Division, NY (2008)

    Google Scholar 

  10. Gotz, D., Wen, Z.: Behavior-driven visualization recommendation. In: Proceedings IUI 2009, pp. 315–324. ACM, New York (2009)

    Google Scholar 

  11. Gotz, D., Zhou, M.X.: Characterizing users’ visual analytic activity for insight provenance. In: 2008 IEEE Symposium on Visual Analytics Science and Technology, VAST 2008, pp. 123–130 (2008)

    Google Scholar 

  12. Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5, 110–141 (1986)

    Article  Google Scholar 

  13. Mackinlay, J., Hanrahan, P., Stolte, C.: Show me: automatic presentation for visual analysis. IEEE Trans. Vis. Comput. Graph. 13(6), 1137–1144 (2007)

    Article  Google Scholar 

  14. Golemati, M., Halatsis, C., Vassilakis, C., Katifori, A., Lepouras, G.: A context-based adaptive visualization environment. In: Proceedings of the conference on Information Visualization, IV 2006, pp. 62–67. IEEE Computer Society, Washington, DC (2006)

    Google Scholar 

  15. Golemati, M., Vassilakis, C., Katifori, A., Lepouras, G., Halatsis, C.: Context and adaptivity-driven visualization method selection. In: Mourlas, C., Germanakos, P. (eds.) Intelligent User Interfaces: Adaptation and Personalization Systems and Technologies. IGI Global (2009)

    Google Scholar 

  16. Bai, X., White, D., Sundaram, D.: Adaptive knowledge visualization systems: a proposal and implementation. IJEEEE 1(3), 193–200 (2011)

    Google Scholar 

  17. Voigt, M., Franke, M., Meiner, K.: Capturing and reusing empirical visualization knowledge. In: UMAP 2013 Extended Proceedings (2013)

    Google Scholar 

  18. de Jongh, M., Dudas, P.M., Brusilovsky, P.: Adaptive visualization of research communities. In: Berkovsky, P., et al. (eds.) UMAP 2013 Extended Proceedings, First International Workshop on User-Adaptive Visualizations (WUAV) (2013)

    Google Scholar 

  19. Keim, D., Mansmann, F., Schneidewind, J., Ziegler, H., Thomas, J.: Visual analytics: scope and challenges. In: Visual Data Mining: Theory, Techniques and Tools for Visual Analytics. LNCS. Springer, December 2008

    Google Scholar 

  20. Nazemi, K., Kohlhammer, J.: Visual variables in adaptive visualizations. In: Extended Proceedings of UMAP 2013, CEUR Workshop Proceedings, vol. 997 (2013). ISSN 1613-0073

    Google Scholar 

  21. Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)

    Article  Google Scholar 

  22. Bertin, J.: Semiology of Graphics. University of Wisconsin Press, Madison (1983)

    Google Scholar 

  23. Ware, C.: Information Visualization Perception for Design. Morgan Kaufmann (Elsevier), Boston (2013)

    Google Scholar 

  24. Rensink, R.A.: Change detection. Annu. Rev. Psychol. 53, 245–277 (2002)

    Article  Google Scholar 

  25. Nazemi, K., Stab, C., Fellner, D.W.: Interaction analysis: an algorithm for interaction prediction and activity recognition in adaptive systems. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, Proceedings, pp. 607–612. IEEE Press, New York (2010)

    Google Scholar 

  26. Nazemi, K., Stab, C., Fellner, D.W.: Interaction Analysis for Adaptive User Interfaces Advanced Intelligent Computing Theories and Applications, pp. 362–371. Springer (2010)

    Google Scholar 

  27. Nazemi, K., Retz, R., Burkhardt, D., Kuijper, A., Kohlhammer, J., Fellner, D.W.: Visual trend analysis with digital libraries. In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business. ACM, New York (2015)

    Google Scholar 

  28. Thomas, J.J., Cook, K.A.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. National Visualization and Analytics Center (2005)

    Google Scholar 

  29. Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F.: Matering the Information Age Solving Problems with Visual Analytics. Eurographics Association (2010)

    Google Scholar 

  30. Munzner, T.: A nested model for visualization design and validation. IEEE Trans. Vis. Comput. Graph. 15(6), 921–928 (2009)

    Article  Google Scholar 

  31. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

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Acknowledgment

This paper is part of the research work of the “Research Group on Digital Communication and Media Innovation”.

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Authors and Affiliations

  1. Darmstadt University of Applied Sciences, Haardtring 100, 64295, Darmstadt, Germany

    Kawa Nazemi

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  1. Kawa Nazemi
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Correspondence to Kawa Nazemi .

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Editors and Affiliations

  1. University of Central Florida, Orlando, Florida, USA

    Waldemar Karwowski

  2. University of Central Florida, Orlando, Florida, USA

    Tareq Ahram

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Nazemi, K. (2018). Intelligent Visual Analytics – a Human-Adaptive Approach for Complex and Analytical Tasks. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration. IHSI 2018. Advances in Intelligent Systems and Computing, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-319-73888-8_29

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  • DOI: https://doi.org/10.1007/978-3-319-73888-8_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73887-1

  • Online ISBN: 978-3-319-73888-8

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