Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together?

  • Andreas Holzinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8127)

Abstract

A major challenge in our networked world is the increasing amount of data, which require efficient and user-friendly solutions. A timely example is the biomedical domain: the trend towards personalized medicine has resulted in a sheer mass of the generated (-omics) data. In the life sciences domain, most data models are characterized by complexity, which makes manual analysis very time-consuming and frequently practically impossible. Computational methods may help; however, we must acknowledge that the problem-solving knowledge is located in the human mind and - not in machines. A strategic aim to find solutions for data intensive problems could lay in the combination of two areas, which bring ideal pre-conditions: Human-Computer Interaction (HCI) and Knowledge Discovery (KDD). HCI deals with questions of human perception, cognition, intelligence, decision-making and interactive techniques of visualization, so it centers mainly on supervised methods. KDD deals mainly with questions of machine intelligence and data mining, in particular with the development of scalable algorithms for finding previously unknown relationships in data, thus centers on automatic computational methods. A proverb attributed perhaps incorrectly to Albert Einstein illustrates this perfectly: “Computers are incredibly fast, accurate, but stupid. Humans are incredibly slow, inaccurate, but brilliant. Together they may be powerful beyond imagination”. Consequently, a novel approach is to combine HCI & KDD in order to enhance human intelligence by computational intelligence.

Keywords

Human-Computer Interaction (HCI) Knowledge Discovery in Data (KDD) HCI-KDD E-Science Interdisciplinary Intersection science 
Download to read the full conference paper text

References

  1. 1.
    Kouzes, R.T., Anderson, G.A., Elbert, S.T., Gorton, I., Gracio, D.K.: The changing paradigm of data-intensive computing. Computer 42, 26–34 (2009)CrossRefGoogle Scholar
  2. 2.
    Hey, T., Gannon, D., Pinkelman, J.: The Future of Data-Intensive Science. Computer 45, 81–82 (2012)CrossRefGoogle Scholar
  3. 3.
    Bell, G., Hey, T., Szalay, A.: Beyond the data deluge. Science 323, 1297–1298 (2009)CrossRefGoogle Scholar
  4. 4.
    Buxton, B., Hayward, V., Pearson, I., Kärkkäinen, L., Greiner, H., Dyson, E., Ito, J., Chung, A., Kelly, K., Schillace, S.: Big data: the next Google. Interview by Duncan Graham-Rowe. Nature 455, 8 (2008)CrossRefGoogle Scholar
  5. 5.
    Holzinger, A.: On Knowledge Discovery and Interactive Intelligent Visualization of Biomedical Data - Challenges in Human–Computer Interaction & Biomedical Informatics. In: DATA 2012, pp. IS9–IS20. INSTICC, Rome (2012)Google Scholar
  6. 6.
    Holzinger, A.: Weakly Structured Data in Health-Informatics: The Challenge for Human-Computer Interaction. In: Baghaei, N., Baxter, G., Dow, L., Kimani, S. (eds.) Proceedings of INTERACT 2011 Workshop: Promoting and Supporting Healthy Living by Design, Lisbon, Portugal. IFIP, pp. 5–7 (2011)Google Scholar
  7. 7.
    Holzinger, A., Stocker, C., Ofner, B., Prohaska, G., Brabenetz, A., Hofmann-Wellenhof, R.: Combining HCI, Natural Language Processing, and Knowledge Discovery - Potential of IBM Content Analytics as an assistive technology in the biomedical field. In: Holzinger, A., Pasi, G. (eds.) HCI-KDD 2013. LNCS, vol. 7947, pp. 13–24. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Holzinger, A.: Biomedical Informatics: Computational Sciences meets Life Sciences. BoD, Norderstedt (2012)Google Scholar
  9. 9.
    Akil, H., Martone, M.E., Van Essen, D.C.: Challenges and opportunities in mining neuroscience data. Science 331, 708–712 (2011)CrossRefGoogle Scholar
  10. 10.
    Dugas, M., Schmidt, K.: Medizinische Informatik und Bioinformatik. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Polanyi, M.: Personal Knowledge: Towards a Post-Critical Philosophy. Nature Publishing Group (1974)Google Scholar
  12. 12.
    Popper, K.R.: Alles Leben ist Problemlösen. Piper, München (1996)Google Scholar
  13. 13.
    Naur, P.: Computing versus human thinking. Communications of the ACM 50, 85–94 (2007)CrossRefGoogle Scholar
  14. 14.
    Naur, P.: The neural embodiment of mental life by the synapse-state theory. Naur. Com Publishing (2008)Google Scholar
  15. 15.
    Shneiderman, B.: Inventing Discovery Tools: Combining Information Visualization with Data Mining. In: Jantke, K.P., Shinohara, A. (eds.) DS 2001. LNCS (LNAI), vol. 2226, pp. 17–28. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Shneiderman, B.: Inventing Discovery Tools: Combining Information Visualization with Data Mining. Information Visualization 1, 5–12 (2002)Google Scholar
  17. 17.
    Shneiderman, B.: Creativity support tools. Communications of the ACM 45, 116–120 (2002)CrossRefGoogle Scholar
  18. 18.
    Shneiderman, B.: Creativity support tools: accelerating discovery and innovation. Communications of the ACM 50, 20–32 (2007)CrossRefGoogle Scholar
  19. 19.
    Butler, D.: 2020 computing: Everything, everywhere. Nature 440, 402–405 (2006)CrossRefGoogle Scholar
  20. 20.
    Simon, H.A.: Designing Organizations for an Information-Rich World. In: Greenberger, M. (ed.) Computers, Communication, and the Public Interest, pp. 37–72. The Johns Hopkins Press, Baltimore (1971)Google Scholar
  21. 21.
    Holzinger, A.: Interacting with Information: Challenges in Human-Computer Interaction and Information Retrieval (HCI-IR). In: IADIS Multiconference on Computer Science and Information Systems (MCCSIS), Interfaces and Human-Computer Interaction, pp. 13–17. IADIS, Rome (2011)Google Scholar
  22. 22.
    Holzinger, A.: Successful Management of Research and Development. BoD, Norderstedt (2011)Google Scholar
  23. 23.
    Von Neumann, J.: The Computer and the Brain. Yale University Press, New Haven (1958)MATHGoogle Scholar
  24. 24.
    Card, S.K., Moran, T.P., Newell, A.: The psychology of Human-Computer Interaction. Erlbaum, Hillsdale (1983)Google Scholar
  25. 25.
    Helander, M. (ed.): Handbook of Human-Computer Interaction. North Holland, Amsterdam (1990)Google Scholar
  26. 26.
    Holzinger, A.: Multimedia Basics. Learning. Cognitive Basics of Multimedia Information Systems, vol. 2. Laxmi-Publications, New Delhi (2002)Google Scholar
  27. 27.
    Ebert, A., Gershon, N., Veer, G.: Human-Computer Interaction. Künstl. Intell. 26, 121–126 (2012)CrossRefGoogle Scholar
  28. 28.
    Hooper, C.J., Dix, A.: Web science and human-computer interaction: forming a mutually supportive relationship. Interactions 20, 52–57 (2013)CrossRefGoogle Scholar
  29. 29.
    Keim, D., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H.: Visual Analytics: Scope and Challenges. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.) Visual Data Mining. LNCS, vol. 4404, pp. 76–90. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  30. 30.
    Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In: Proceedings of the 1996 IEEE Symposium on Visual Languages, pp. 336–343 (1996)Google Scholar
  31. 31.
    Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F. (eds.): Mastering the Information Age: Solving Problems with Visual Analytics. Eurographics, Goslar (2010)Google Scholar
  32. 32.
    Van Wijk, J.J.: The value of visualization. In: Visualization, VIS 2005, pp. 79–86. IEEE (2005)Google Scholar
  33. 33.
    Dervin, B.: Sense-making theory and practice: an overview of user interests in knowledge seeking and use. J. Knowl. Manag. 2, 36–46 (1998)CrossRefGoogle Scholar
  34. 34.
    Beale, R.: Supporting serendipity: Using ambient intelligence to augment user exploration for data mining and Web browsing. International Journal of Human-Computer Studies 65, 421–433 (2007)CrossRefGoogle Scholar
  35. 35.
    Holzinger, A., Kickmeier-Rust, M., Albert, D.: Dynamic Media in Computer Science Education; Content Complexity and Learning Performance: Is Less More? Educational Technology & Society 11, 279–290 (2008)Google Scholar
  36. 36.
    Ceglar, A., Roddick, J., Calder, P.: Chapter 4: Guiding Knowledge Discovery through Interactive Data Mining. In: Pendharkar, P. (ed.) Managing Data Mining Technologies in Organizations: Techniques and Applications, pp. 45–86. Idea Group Publishing, Hershey (2003)Google Scholar
  37. 37.
    Chau, D.H., Myers, B., Faulring, A.: What to do when search fails: finding information by association. In: Proceeding of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 999–1008. ACM, Florence (2008)CrossRefGoogle Scholar
  38. 38.
    Shiffrin, R.M., Gardner, G.T.: Visual Processing Capacity and Attention Control. Journal of Experimental Psychology 93, 72 (1972)CrossRefGoogle Scholar
  39. 39.
    Kahneman, D.: Attention and Effort. Prentice-Hall, Englewood Cliffs (1973)Google Scholar
  40. 40.
    Duncan, J.: Selective attention and the organization of visual information. Journal of Experimental Psychology: General 113, 501–517 (1984)CrossRefGoogle Scholar
  41. 41.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection: A Survey. ACM Computing Surveys 41 (2009)Google Scholar
  42. 42.
    Holzinger, A., Kickmeier-Rust, M.D., Wassertheurer, S., Hessinger, M.: Learning performance with interactive simulations in medical education: Lessons learned from results of learning complex physiological models with the HAEMOdynamics SIMulator. Computers & Education 52, 292–301 (2009)CrossRefGoogle Scholar
  43. 43.
    Lazar, J., Feng, J.H., Hochheiser, H.: Research Methods in Human-Computer Interaction. Wiley, Chichester (2010)Google Scholar
  44. 44.
    Cairns, P., Cox, A.L. (eds.): Research Methods for Human-Computer Interaction. Cambridge University Press, Cambridge (2008)Google Scholar
  45. 45.
    Nestor, P.G., Schutt, R.K.: Research Methods in Psychology: Investigating Human Behavior. Sage Publications (2011)Google Scholar
  46. 46.
    Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, Heidelberg (2010)MATHGoogle Scholar
  47. 47.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2011)Google Scholar
  48. 48.
    Piatetsky-Shapiro, G.: Knowledge discovery in databases: 10 years after. ACM SIGKDD Explorations Newsletter 1, 59–61 (2000)CrossRefGoogle Scholar
  49. 49.
    Blum, R.L., Wiederhold, G.C.: Studying hypotheses on a time-oriented clinical database: an overview of the RX project. In: Computer-Assisted Medical Decision Making, pp. 245–253. Springer (1985)Google Scholar
  50. 50.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39, 27–34 (1996)CrossRefGoogle Scholar
  51. 51.
    Piateski, G., Frawley, W.: Knowledge discovery in databases. MIT Press, Cambridge (1991)Google Scholar
  52. 52.
    Cios, J., Pedrycz, W., Swiniarski, R.: Data Mining in Knowledge Discovery. Academic Publishers (1998)Google Scholar
  53. 53.
    Liu, H., Motoda, H.: Feature selection for knowledge discovery and data mining. Springer, Heidelberg (1998)MATHCrossRefGoogle Scholar
  54. 54.
    Fayyad, U.M., Wierse, A., Grinstein, G.G.: Information visualization in data mining and knowledge discovery. Morgan Kaufmann Pub. (2002)Google Scholar
  55. 55.
    Billinger, M., Brunner, C., Scherer, R., Holzinger, A., Müller-Putz, G.: Towards a framework based on single trial connectivity for enhancing knowledge discovery in BCI. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds.) AMT 2012. LNCS, vol. 7669, pp. 658–667. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  56. 56.
    Holzinger, A., Scherer, R., Seeber, M., Wagner, J., Müller-Putz, G.: Computational Sensemaking on Examples of Knowledge Discovery from Neuroscience Data: Towards Enhancing Stroke Rehabilitation. In: Böhm, C., Khuri, S., Lhotská, L., Renda, M.E. (eds.) ITBAM 2012. LNCS, vol. 7451, pp. 166–168. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  57. 57.
    Holzinger, A., Stocker, C., Peischl, B., Simonic, K.-M.: On Using Entropy for Enhancing Handwriting Preprocessing. Entropy 14, 2324–2350 (2012)CrossRefGoogle Scholar
  58. 58.
    Holzinger, A., Stocker, C., Bruschi, M., Auinger, A., Silva, H., Gamboa, H., Fred, A.: On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds.) AMT 2012. LNCS, vol. 7669, pp. 646–657. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  59. 59.
    Petz, G., Karpowicz, M., Fürschuß, H., Auinger, A., Winkler, S.M., Schaller, S., Holzinger, A.: On text preprocessing for opinion mining outside of laboratory environments. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds.) AMT 2012. LNCS, vol. 7669, pp. 618–629. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  60. 60.
    Petz, G., Karpowicz, M., Fürschuß, H., Auinger, A., Stříteský, V., Holzinger, A.: Opinion Mining on the Web 2.0 – Characteristics of User Generated Content and Their Impacts. In: Holzinger, A., Pasi, G. (eds.) HCI-KDD 2013. LNCS, vol. 7947, pp. 35–46. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  61. 61.
    Holzinger, A., Zupan, M.: KNODWAT: A scientific framework application for testing knowledge discovery methods for the biomedical domain. BMC Bioinformatics 14, 191 (2013)CrossRefGoogle Scholar
  62. 62.
    Holzinger, A.: Process Guide for Students for Interdisciplinary Work in Computer Science/Informatics, 2nd edn. BoD, Norderstedt (2010)Google Scholar
  63. 63.
    Mobjörk, M.: Consulting versus participatory transdisciplinarity: A refined classification of transdisciplinary research. Futures 42, 866–873 (2010)CrossRefGoogle Scholar
  64. 64.
    Wickson, F., Carew, A.L., Russell, A.W.: Transdisciplinary research: characteristics, quandaries and quality. Futures 38, 1046–1059 (2006)CrossRefGoogle Scholar
  65. 65.
    Lawrence, R.J., Després, C.: Futures of Transdisciplinarity. Futures 36, 397–405 (2004)CrossRefGoogle Scholar
  66. 66.
  67. 67.
    Funk, P., Xiong, N.: Case-based reasoning and knowledge discovery in medical applications with time series. Comput. Intell. 22, 238–253 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Andreas Holzinger
    • 1
    • 2
  1. 1.Institute for Medical Informatics, Statistics and Documentation, Research Unit Human-Computer InteractionMedical University GrazGrazAustria
  2. 2.Institute for Information Systems and Computer MediaGraz University of TechnologyGrazAustria

Personalised recommendations