Advertisement

A Knowledge-Based Conceptual Modelling Approach to Bridge Design Thinking and Intelligent Environments

  • Michael WalchEmail author
  • Takeshi Morita
  • Dimitris Karagiannis
  • Takahira Yamaguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11775)

Abstract

One aspect of knowledge management is concerned with the alignment between what is captured in the heads of people and what is encoded by technology. The alignment of knowledge is necessary as humans possess an efficient ability to design innovation based on business insights, while technological systems are able to operating efficiently in different environments. To support knowledge management, this study presents systematic foundations covering a knowledge-based conceptual modelling approach. On a systematic level, three procedures are presented to facilitate the alignment of knowledge between people and technology: the decomposition of concepts from design thinking in conceptual models, the abstraction of capabilities from intelligent environments in conceptual models, and the (semi-) automated, intelligent transformation of conceptual models. Furthermore, the architecture of ICT infrastructure supporting the three procedures is addressed. These systematic foundations are integrated in the OMiLAB ecosystem and instantiated in two projects. The first project revolves around PRINTEPS, which is a framework to develop practical Artificial Intelligence. The second project revolves around s*IoT, which is a unifying semantic-aware modelling environment for the Internet of Things. Additionally, two concrete cases are presented for both project. Due to employing common systematic foundations, transfer and reuse among the two projects is facilitated.

Keywords

Knowledge creation and acquisition Knowledge and data integration Conceptual modelling in knowledge-based systems 

Notes

Acknowledgement

A part of this study was supported by the project of “A Framework PRINTEPS to Develop Practical Artificial Intelligence,” (JPMJCR14E3) the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST).

References

  1. 1.
    Li, G., Tan, J., Chaudhry, S.S.: Industry 4.0 and big data innovations. Enterp. Inf. Syst. 13(2), 145–147 (2019)CrossRefGoogle Scholar
  2. 2.
    Stephanedes, Y.J., Golias, M., Dedes, G., Douligeris, C., Mishra, S.: Challenges, risks and opportunities for connected vehicle services in smart cities and communities. In: 2nd IFAC Conference on Cyber-Physical and Human Systems CPHS 2018, vol. 51, no. 34, pp. 139–144 (2019). IFAC-PapersOnLineGoogle Scholar
  3. 3.
    Maiden, N., Zachos, K., Paraskevopoulos, F., Lentsek, S., Apostolou, D., Mentzas, G.: Creative information exploration in journalism. In: The 9th International Conference on Information, Intelligence, Systems and Applications, IISA2018 (2018)Google Scholar
  4. 4.
    Zablith, F., et al.: Ontology evolution: a process-centric survey. Knowl. Eng. Rev. 30(1), 45–75 (2015)CrossRefGoogle Scholar
  5. 5.
    Morita, T., Nakamura, K., Komatsushiro, H., Yamaguchi, T.: PRINTEPS: an integrated intelligent application development platform based on stream reasoning and ROS. Rev. Socionetwork Strat. 12, 71–96 (2018)CrossRefGoogle Scholar
  6. 6.
    Walch, M., Karagiannis, D.: How to connect design thinking and cyber-physical systems: the s*IoT conceptual modelling approach. In: 52nd Hawaii International Conference on System Sciences, HICSS 2019, Wailea, USA, 8–11 Jannuary 2019 (2019)Google Scholar
  7. 7.
    Nye, E., Melendez-Torres, G., Bonell, C.: Origins, methods and advances in qualitative meta-synthesis. Rev. Educ. 4(1), 57–79 (2016)CrossRefGoogle Scholar
  8. 8.
    Twinomurinzi, H., Johnson, R.: Meta-synthesizing qualitative research in information systems. J. Community Inf. 11(3) (2015)Google Scholar
  9. 9.
    Hevner, A., Chatterjee, S.: Design Research in Information Systems: Theory and Practice, vol. 22. Springer Science & Business Media, Boston (2010).  https://doi.org/10.1007/978-1-4419-5653-8CrossRefGoogle Scholar
  10. 10.
    Vom Brocke, J., Buddendick, C.: Reusable conceptual models-requirements based on the design science research paradigm. In: Proceedings of the First International Conference on Design Science Research in Information Systems and Technology (DESRIST), pp. 576–604. Citeseer (2006)Google Scholar
  11. 11.
    Muck, C., Miron, E.-T., Karagiannis, D., Moonkun, L.: Supporting service design with storyboards and diagrammatic models: the Scene2Model tool. In: Joint International Conference of Service Science and Innovation (ICSSI 2018) and Serviceology (ICServ 2018), September 2018Google Scholar
  12. 12.
    Morita, T., et al.: Practice of multi-robot teahouse based on PRINTEPS and evaluation of service quality. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018, Tokyo, Japan, 23–27 July 2018, vol. 2, pp. 147–152 (2018)Google Scholar
  13. 13.
    Bork, D., Buchmann, R., Karagiannis, D., Lee, M., Miron, E.-T.: An open platform for modeling method conceptualization: the OMiLAB digital ecosystem. Commun. Assoc. Inf. Syst. (2018)Google Scholar
  14. 14.
    Fill, H.-G., Karagiannis, D.: On the conceptualisation of modelling methods using the ADOxx meta modelling platform. Enterp. Model. Inf. Syst. Architectures Int. J. 8(1), 4–25 (2013)CrossRefGoogle Scholar
  15. 15.
    Karagiannis, D.: Agile modeling method engineering. In: Proceedings of the 19th Panhellenic Conference on Informatics, PCI 2015, New York, NY, USA, pp. 5–10. ACM (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michael Walch
    • 1
    Email author
  • Takeshi Morita
    • 2
  • Dimitris Karagiannis
    • 1
  • Takahira Yamaguchi
    • 2
  1. 1.University of ViennaViennaAustria
  2. 2.Keio UniversityYokohamaJapan

Personalised recommendations