A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process

Lauri Tuovinen

2016

Abstract

The knowledge discovery process is traditionally viewed as a sequence of operations to be applied to data; the human aspect of the process is seldom taken into account, and when it is, it is mainly the roles and actions of domain and technology experts that are considered. However, non-experts can also play an important role in knowledge discovery, and furthermore, the role of technology in the process may also be substantially expanded from what it traditionally has been, with special software facilitating interactions among human actors and even operating as an actor in its own right. This diversification of the knowledge discovery process is helpful in finding tenable solutions to the new problems presented by the current deluge of digital data, but only if the process model used to manage the process adequately represents the variety of forms that the process can take. The paper addresses this requirement by presenting a conceptual model that can be used to describe different types of knowledge discovery processes in terms of the actors involved and the interactions they have with one another. Additionally, the paper discusses how the interactions can be facilitated to provide effective support for each different type of process. As a future perspective, the paper considers the implications of intelligent software taking on responsibilities traditionally reserved for human actors.

References

  1. Brachman, R. J. and Anand, T. (1996). The process of knowledge discovery in databases. In Advances in Knowledge Discovery and Data Mining, pages 37-57. American Association for Artificial Intelligence.
  2. Büchner, A. G., Mulvenna, M. D., Anand, S. S., and Hughes, J. G. (1999). An internet-enabled knowledge discovery process. In Proceedings of the 9th International Database Conference, pages 13-27.
  3. Diamantini, C., Potena, D., Domenico, and Smari, W. W. (2006). Collaborative knowledge discovery in databases: A knowledge exchange perspective. In Proceedings of the AAAI Fall Symposium on Semantic Web for Collaborative Knowledge Acquisition, pages 24-31.
  4. Donalek, C., Djorgovski, S. G., Cioc, A., Wang, A., Zhang, J., Lawler, E., Yeh, S., Mahabal, A., Graham, M., Drake, A., Davidoff, S., Norris, J. S., and Longo, G. (2014). Immersive and collaborative data visualization using virtual reality platforms. In Proceedings of the 2014 IEEE International Conference on Big Data, pages 609-614.
  5. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). The kdd process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11):27-34.
  6. Federal Big Data Commission (2012). Demystifying big data: A practical guide to transforming the business of government. Technical report, TechAmerica Foundation.
  7. Haffar, J. (2015). Have you seen ASUM-DM? Blog entry, retrieved 16 Sep, 2016. https://developer.ibm.com/ predictiveanalytics/2015/10/16/have-you-seen-asumdm/.
  8. Holzinger, A. (2013). Human-computer interaction and knowledge discovery (HCI-KDD): What is the benefit of bringing those two fields to work together? In Availability, Reliability, and Security in Information Systems and HCI: Proceedings of the International Cross-Domain Conference and Workshop, pages 319- 328.
  9. Horeis, T. and Sick, B. (2007). Collaborative knowledge discovery & data mining: From knowledge to experience. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, pages 421-428.
  10. Klusch, M., Lodi, S., and Moro, G. (2003). Agentbased distributed data mining: The KDEC scheme. In Klusch, M., Bergamaschi, S., Edwards, P., and Petta, P., editors, Intelligent Information Agents: The AgentLink Perspective, pages 104-122. Springer Berlin Heidelberg.
  11. Korpela, E. J., Siemion, A. P. V., Werthimer, D., Lebofsky, M., Cobb, J., Croft, S., and Anderson, D. (2015). The next phases of SETI@home. In Proceedings of SPIE 9606, Instruments, Methods, and Missions for Astrobiology XVII.
  12. Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., Raddick, M. J., Nichol, R. C., Szalay, A., Andreescu, D., Murray, P., and Vandenberg, J. (2008). Galaxy Zoo: Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society, 389(3):1179-1189.
  13. Mariscal, G., Marbán, O., and Fernández, C. (2010). A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review, 25(2):137-166.
  14. McCormick, K. (2007). CRISP-DM 2.0. Blog entry, retrieved 16 Sep, 2016. http://keithmccormick.com/ crisp-dm-20/.
  15. Moyle, S. and Jorge, A. (2001). RAMSYS - a methodology for supporting rapid remote collaborative data mining projects. In ECML/PKKD01 Workshop on Integrating Aspects of Data Mining, Decision Support and MetaLearning, pages 20-31.
  16. Serban, F., Vanschoren, J., Kietz, J.-U., and Bernstein, A. (2013). A survey of intelligent assistants for data analysis. ACM Computing Surveys, 45(3):article 31.
  17. Shah, A. and Gulati, R. (2015). Contemporary trends in privacy preserving collaborative data mining - a survey. In Proceedings of the 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization.
  18. Sun, G.-D., Liang, R.-H., and Liu, S.-X. (2013). A survey of visual analytics techniques and applications: Stateof-the-art research and future challenges. Journal of Computer Science and Technology, 28(5):852-867.
  19. Swan, M. (2013). The quantified self: fundamental disruption in big data science and biological discovery. Big Data, 1(2):85-99.
  20. Terzi, D. S., Terzi, R., and Sagiroglu, S. (2015). A survey on security and privacy issues in big data. In Proceedings of the 10th International Conference for Internet Technology and Secured Transactions, pages 202-207.
  21. Tuovinen, L. (2014). From machine learning to learning with machines: Remodeling the knowledge discovery process. PhD thesis. University of Oulu, Finland.
  22. Tuovinen, L. and Röning, J. (2009). Everybody wins: Challenges and promises of knowledge discovery through volunteer computing. In Proceedings of the 8th International Conference on Computer Ethics: Philosophical Enquiry, pages 821-842.
  23. Wirth, R. and Hipp, J. (2000). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pages 29-39.
  24. Zhang, L., Stoffel, A., Behrisch, M., Mittelstadt, S., Schreck, T., Pompl, R., Weber, S., Last, H., and Keim, D. (2012). Visual analytics for the big data era - a comparative review of state-of-the-art commercial systems. In Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology, pages 173-182.
Download


Paper Citation


in Harvard Style

Tuovinen L. (2016). A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 240-248. DOI: 10.5220/0006045902400248


in Bibtex Style

@conference{kdir16,
author={Lauri Tuovinen},
title={A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={240-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006045902400248},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process
SN - 978-989-758-203-5
AU - Tuovinen L.
PY - 2016
SP - 240
EP - 248
DO - 10.5220/0006045902400248