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Method for Assessing the Applicability of AI Service Systems

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Human Centred Intelligent Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 189))

Abstract

In this study, we consider the application of Artificial Intelligence (AI) technologies to enterprise functions and evaluate the viability of the AI service system. To assess applicability, we introduced conditions by modeling the business task and the AI service system by using the Enterprise Architecture approach. Through investigation, we confirmed that the proposed conditions were appropriate to the target business domain to ensure that the AI service system is relevant.

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Notes

  1. 1.

    https://www.kdnuggets.com/2018/07/why-machine-learning-project-fail.html.

References

  1. Amershi, S., Begel, A., Bird, C., Deliner, R., Gall, H., Kamar, E., Nushi, N.N.B., Zimmermann, T.: Software engineering for machine learning: a case study. In: Proceedings of the 41st International Conference on Software Engineering, pp. 291–300 (2019)

    Google Scholar 

  2. Baclawski, K., Chan, E.S., Gawlick, D., Liu, Z.H., Ghoneimy, A., Gross, K., Zhang, X.: Framework for ontology-driven decision making. Appl. Ontol. 12(3–4), 245–273 (2017)

    Article  Google Scholar 

  3. Demchenko, Y., de Last, C., Membrey, P.: Defining architecture components of the big data ecosystem. In: Proceedings of the International Conference on Collaboration Technologies and Systems (CTS), pp. 104–112 (2014)

    Google Scholar 

  4. Earley, S.: Analytics, machine learning, and the internet of things. IEEE ITPro 17(1), 10–13 (2015)

    Google Scholar 

  5. Gawlick, D., Chan, E.S., Ghoneimy, A., Liu, Z.H.: Mastering situation awareness: the next big challenge? SIGMOD Rec. 44(3), 19–24 (2015)

    Article  Google Scholar 

  6. Heit, J., Liu, J., Shah, M.: An architecture for the deployment of statistical models for the big data era. In: Proceedings of IEEE International Conference on Big Data, pp. 1377–1384 (2016)

    Google Scholar 

  7. Hinkelmann, K., Gerber, A., Karagiannis, D., Thoenssen, B., van der Merwe, A., Woitsch, R.: A new paradigm for the continuous alignment of business and IT: combining enterprise architecture modelling and enterprise ontology. Comput. Ind. 79, 77–86 (2016)

    Article  Google Scholar 

  8. Kim, M., Zimmermann, T., DeLine, R., Begel, A.: The emerging role of data scientists on software development teams. In: Proceedings of the 38th International Conference on Software Engineering, pp. 96–107 (2016)

    Google Scholar 

  9. Masood, A., Hashmi, A.: Cognitive Computing Recipes: Artificial Intelligence Solutions using Microsoft Cognitive Services and TensorFlow. Apress (2019)

    Google Scholar 

  10. Saat, J., Franke, U., Lagerström, R., Ekstedt, M.: Enterprise architecture meta models for IT/business alignment situations. In: Proceedings of the 14th IEEE International Enterprise Distributed Object Computing Conference, pp. 14–23 (2010)

    Google Scholar 

  11. Sternberg, R.J.: Successful Intelligence: How Practical and Creative Intelligence Determines Success in Life. Simon & Schuster (1996)

    Google Scholar 

  12. Takeuchi, H., Yamamoto, S.: Business AI alignment modeling based on enterprise architecture. In: Proceedings of the 11th KES International Conference on Intelligent Decision Technologies, pp. 155–165 (2019)

    Chapter  Google Scholar 

  13. The Open Group: ArchiMate 3.0.1—A Pocket Guide. Van Haren Publishing (2013)

    Google Scholar 

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Acknowledgements

This work was supported by JSPS Grants-in-Aid for Scientific Research (KAKENHI) Grant Number JP19K20416.

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Correspondence to Hironori Takeuchi .

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Takeuchi, H., Yamamoto, S. (2021). Method for Assessing the Applicability of AI Service Systems. In: Zimmermann, A., Howlett, R., Jain, L. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 189. Springer, Singapore. https://doi.org/10.1007/978-981-15-5784-2_26

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