A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11845)


Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.


Visual Analytics Information visualization Trend analytics Analysis approach User-centered design 



This work was partially funded by the Hessen State Ministry for Higher Education, Research and the Arts within the program “Forschung für die Praxis” and was conducted within the research group on Human-Computer Interaction and Visual Analytics ( The presentation of this work was supported by the Research Center for Digital Communication & Media Innovation of the Darmstadt University of Applied Sciences.


  1. 1.
    Havre, S., Hetzler, E., Whitney, P., Nowell, L.: ThemeRiver: visualizing thematic changes in large document collections. IEEE TVCG 8(1), 9–20 (2002). Scholar
  2. 2.
    Liu, S., et al.: TIARA: interactive, topic-based visual text summarization and analysis. ACM Trans. Intell. Syst. Technol. 3(2), 25:1–25:28 (2012)Google Scholar
  3. 3.
    Dou, W., Wang, X., Chang, R., Ribarsky, W.: ParallelTopics: a probabilistic approach to exploring document collections. In: VAST 2011 (2011)Google Scholar
  4. 4.
    Collins, C., Viegas, F., Wattenberg, M.: Parallel tag clouds to explore and analyze faceted text corpora. In: VAST 2009 (2009)Google Scholar
  5. 5.
    Lee, B., Riche, N.H., Karlson, A.K., Carpendale, S.: SparkClouds: visualizingtrends in tag clouds. IEEE TVCG 16, 1182–1189 (2010)Google Scholar
  6. 6.
    Lohmann, S., Burch, M., Schmauder, H., Weiskopf, D.: Visual analysis of microblog content using time-varying co-occurrence highlighting in tag clouds. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI 2012, pp. 753–756. ACM, New York (2012)Google Scholar
  7. 7.
    Han, Q., Heimerl, F., Codina-Filba, J., Lohmann, S., Wanner, L., Ertl, T.: Visual patent trend analysis for informed decision making in technology management. World Patent Inf. 49, 34–42 (2017)CrossRefGoogle Scholar
  8. 8.
    Heimerl, F., Han, Q., Koch, S., Ertl, T.: CiteRivers: visual analytics of citation patterns. IEEE Trans. Vis. Comput. Graph. 22(1), 190–199 (2016)CrossRefGoogle Scholar
  9. 9.
    Wei, F., et al.: TIARA: a visual exploratory text analytic system. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 153–162. ACM, New York (2010)Google Scholar
  10. 10.
    Ernst, H.: Patent information for strategic technology management. World Patent Inf. 25(3), 233–242 (2003)CrossRefGoogle Scholar
  11. 11.
    Joho, H., Azzopardi, L.A., Vanderbauwhede, W.: A survey of patent users: an analysis of tasks, behavior, search functionality and system requirements. In: Proceedings of the Third Symposium on Information Interaction in Context, IIiX 2010, pp. 13–24. ACM, New York (2010)Google Scholar
  12. 12.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: VL, pp. 336–343 (1996)Google Scholar
  13. 13.
    van Ham, F., Perer, A.: Search, show context, expand on demand: supporting large graph exploration with degree-of-interest. IEEE Trans. Vis. Comput. Graph. 15, 953–960 (2009)CrossRefGoogle Scholar
  14. 14.
    Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)CrossRefGoogle Scholar
  15. 15.
    Bloom, B.S.: Taxonomy of Educational Objectives. David McKay Co., Inc., New York (1956)Google Scholar
  16. 16.
    White, R.W., Roth, R.A.: Exploratory Search: Beyond the Query-Response Paradigm. Synthesis Lectures on Information Concepts, Retrieval, and Services. Marchionini, G. (ed.), vol. 1. Morgan & Claypool Publishers (2009)Google Scholar
  17. 17.
    Bruner, J.S.: The act of discovery. Harvard Educ. Rev. 31, 21–32 (1961)Google Scholar
  18. 18.
    Bonino, D., Ciaramella, A., Corno, F.: Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics. World Patent Inf. 32(1), 30–38 (2010)CrossRefGoogle Scholar
  19. 19.
    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 - i-KNOW 2015. ACM Press (2015).
  20. 20.
    Nazemi, K., Burkhard, D.: Visual analytics for analyzing technological trends from text. In: 2019 23rd International Conference Information Visualisation (IV), pp. 191–200. IEEE (2019)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Research Group on Human-Computer Interaction and Visual AnalyticsDarmstadt University of Applied SciencesDarmstadtGermany

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