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.
Chapter PDF
Similar content being viewed by others
Keywords
References
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)
Hey, T., Gannon, D., Pinkelman, J.: The Future of Data-Intensive Science. Computer 45, 81–82 (2012)
Bell, G., Hey, T., Szalay, A.: Beyond the data deluge. Science 323, 1297–1298 (2009)
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)
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)
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)
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)
Holzinger, A.: Biomedical Informatics: Computational Sciences meets Life Sciences. BoD, Norderstedt (2012)
Akil, H., Martone, M.E., Van Essen, D.C.: Challenges and opportunities in mining neuroscience data. Science 331, 708–712 (2011)
Dugas, M., Schmidt, K.: Medizinische Informatik und Bioinformatik. Springer, Heidelberg (2003)
Polanyi, M.: Personal Knowledge: Towards a Post-Critical Philosophy. Nature Publishing Group (1974)
Popper, K.R.: Alles Leben ist Problemlösen. Piper, München (1996)
Naur, P.: Computing versus human thinking. Communications of the ACM 50, 85–94 (2007)
Naur, P.: The neural embodiment of mental life by the synapse-state theory. Naur. Com Publishing (2008)
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)
Shneiderman, B.: Inventing Discovery Tools: Combining Information Visualization with Data Mining. Information Visualization 1, 5–12 (2002)
Shneiderman, B.: Creativity support tools. Communications of the ACM 45, 116–120 (2002)
Shneiderman, B.: Creativity support tools: accelerating discovery and innovation. Communications of the ACM 50, 20–32 (2007)
Butler, D.: 2020 computing: Everything, everywhere. Nature 440, 402–405 (2006)
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)
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)
Holzinger, A.: Successful Management of Research and Development. BoD, Norderstedt (2011)
Von Neumann, J.: The Computer and the Brain. Yale University Press, New Haven (1958)
Card, S.K., Moran, T.P., Newell, A.: The psychology of Human-Computer Interaction. Erlbaum, Hillsdale (1983)
Helander, M. (ed.): Handbook of Human-Computer Interaction. North Holland, Amsterdam (1990)
Holzinger, A.: Multimedia Basics. Learning. Cognitive Basics of Multimedia Information Systems, vol. 2. Laxmi-Publications, New Delhi (2002)
Ebert, A., Gershon, N., Veer, G.: Human-Computer Interaction. Künstl. Intell. 26, 121–126 (2012)
Hooper, C.J., Dix, A.: Web science and human-computer interaction: forming a mutually supportive relationship. Interactions 20, 52–57 (2013)
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)
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)
Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F. (eds.): Mastering the Information Age: Solving Problems with Visual Analytics. Eurographics, Goslar (2010)
Van Wijk, J.J.: The value of visualization. In: Visualization, VIS 2005, pp. 79–86. IEEE (2005)
Dervin, B.: Sense-making theory and practice: an overview of user interests in knowledge seeking and use. J. Knowl. Manag. 2, 36–46 (1998)
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)
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)
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)
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)
Shiffrin, R.M., Gardner, G.T.: Visual Processing Capacity and Attention Control. Journal of Experimental Psychology 93, 72 (1972)
Kahneman, D.: Attention and Effort. Prentice-Hall, Englewood Cliffs (1973)
Duncan, J.: Selective attention and the organization of visual information. Journal of Experimental Psychology: General 113, 501–517 (1984)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection: A Survey. ACM Computing Surveys 41 (2009)
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)
Lazar, J., Feng, J.H., Hochheiser, H.: Research Methods in Human-Computer Interaction. Wiley, Chichester (2010)
Cairns, P., Cox, A.L. (eds.): Research Methods for Human-Computer Interaction. Cambridge University Press, Cambridge (2008)
Nestor, P.G., Schutt, R.K.: Research Methods in Psychology: Investigating Human Behavior. Sage Publications (2011)
Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, Heidelberg (2010)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2011)
Piatetsky-Shapiro, G.: Knowledge discovery in databases: 10 years after. ACM SIGKDD Explorations Newsletter 1, 59–61 (2000)
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)
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)
Piateski, G., Frawley, W.: Knowledge discovery in databases. MIT Press, Cambridge (1991)
Cios, J., Pedrycz, W., Swiniarski, R.: Data Mining in Knowledge Discovery. Academic Publishers (1998)
Liu, H., Motoda, H.: Feature selection for knowledge discovery and data mining. Springer, Heidelberg (1998)
Fayyad, U.M., Wierse, A., Grinstein, G.G.: Information visualization in data mining and knowledge discovery. Morgan Kaufmann Pub. (2002)
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)
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)
Holzinger, A., Stocker, C., Peischl, B., Simonic, K.-M.: On Using Entropy for Enhancing Handwriting Preprocessing. Entropy 14, 2324–2350 (2012)
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)
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)
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)
Holzinger, A., Zupan, M.: KNODWAT: A scientific framework application for testing knowledge discovery methods for the biomedical domain. BMC Bioinformatics 14, 191 (2013)
Holzinger, A.: Process Guide for Students for Interdisciplinary Work in Computer Science/Informatics, 2nd edn. BoD, Norderstedt (2010)
Mobjörk, M.: Consulting versus participatory transdisciplinarity: A refined classification of transdisciplinary research. Futures 42, 866–873 (2010)
Wickson, F., Carew, A.L., Russell, A.W.: Transdisciplinary research: characteristics, quandaries and quality. Futures 38, 1046–1059 (2006)
Lawrence, R.J., Després, C.: Futures of Transdisciplinarity. Futures 36, 397–405 (2004)
http://www.benshoemate.com/2008/11/30/einstein-never-said-that/
Funk, P., Xiong, N.: Case-based reasoning and knowledge discovery in medical applications with time series. Comput. Intell. 22, 238–253 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Holzinger, A. (2013). Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together?. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds) Availability, Reliability, and Security in Information Systems and HCI. CD-ARES 2013. Lecture Notes in Computer Science, vol 8127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40511-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-40511-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40510-5
Online ISBN: 978-3-642-40511-2
eBook Packages: Computer ScienceComputer Science (R0)