Skip to main content
SpringerLink
Log in

Towards a Software Engineering Framework for the Design, Construction and Deployment of Machine Learning-Based Solutions in Digitalization Processes

  • Conference paper
  • First Online:
Research & Innovation Forum 2019 (RIIFORUM 2019)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Included in the following conference series:

Abstract

There is an increasing demand of digitalization technologies in almost all aspects in modern life. A swelling part of these technologies and solutions are based on Machine Learning technologies. As a consequence of this, there is a need to develop these solutions in a sound and solid way to increase software quality in its eight characteristics: functional suitability, reliability, performance efficiency, usability, security, compatibility, maintainability and portability. To do so, it is needed to adopt software engineering and information systems standards to support the process. This paper aims to draw the path towards a framework to support digitalization processes based on machine-learning solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Syam, N., Sharma, A.: Waiting for a sales renaissance in the fourth industrial revolution: machine learning and artificial intelligence in sales research and practice. Ind. Mark. Manag. 69, 135–146 (2018). https://doi.org/10.1016/j.indmarman.2017.12.019

    Article  Google Scholar 

  2. Marr, B.: Why Everyone Must Get Ready For The 4th Industrial Revolution. https://www.forbes.com/sites/bernardmarr/2016/04/05/why-everyone-must-get-ready-for-4th-industrial-revolution/ (2016)

  3. Canetta, L., Barni, A., Montini, E.: Development of a digitalization maturity model for the manufacturing sector. In: 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1–7 (2018). https://doi.org/10.1109/ICE.2018.8436292

  4. Wang, S., Wan, J., Zhang, D., Li, D., Zhang, C.: Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 101, 158–168 (2016). https://doi.org/10.1016/j.comnet.2015.12.017

    Article  Google Scholar 

  5. BCG: Digitalization Strategy Framework. https://www.bcg.com/capabilities/technology-digital/digitalization-strategy-framework.aspx

  6. Cognizant: A Framework for Digital Business Transformation, https://www.cognizant.com/InsightsWhitepapers/a-framework-for-digital-business-transformation-codex-1048.pdf

  7. Schmarzo, B.: Digital Business Transformation Framework, https://www.cio.com/article/3130103/analytics/digital-business-transformation-framework.html

  8. Catlin, T., Lorenz, J.-T., Sternfels, B., Willmott, P.: A roadmap for a digital transformation| McKinsey. https://www.mckinsey.com/industries/financial-services/our-insights/a-roadmap-for-a-digital-transformation

  9. Bin-Abbas, H., Bakry, S.H.: Assessment of IT governance in organizations: a simple integrated approach. Comput. Hum. Behav. 32, 261–267 (2014). https://doi.org/10.1016/j.chb.2013.12.019

    Article  Google Scholar 

  10. Oliver, D., Lainhart, J.: COBIT 5: adding value through effective geit. EDPACS 46, 1–12 (2012). https://doi.org/10.1080/07366981.2012.706472

    Article  Google Scholar 

  11. COBIT 5: A Business Framework for the Governance and Management of Enterprise IT. http://www.isaca.org/cobit/

  12. Lu, Y.: Industry 4.0: A survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 6, 1–10 (2017). https://doi.org/10.1016/j.jii.2017.04.005

    Article  Google Scholar 

  13. Oliff, H., Liu, Y.: Towards industry 4.0 utilizing data-mining techniques: a case study on quality improvement. Proc. CIRP. 63, 167–172 (2017). https://doi.org/10.1016/j.procir.2017.03.311

    Article  Google Scholar 

  14. Miškuf, M., Zolotová, I.: Comparison between multi-class classifiers and deep learning with focus on industry 4.0. In: 2016 Cybernetics Informatics (K I), pp. 1–5 (2016). https://doi.org/10.1109/CYBERI.2016.7438633

  15. Lee, J., Davari, H., Singh, J., Pandhare, V.: Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 18, 20–23 (2018). https://doi.org/10.1016/j.mfglet.2018.09.002

    Article  Google Scholar 

  16. Traub, M., Vögel, H., Sax, E., Streichert, T., Härri, J.: Digitalization in automotive and industrial systems. In: 2018 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 1203–1204 (2018). https://doi.org/10.23919/DATE.2018.8342198

  17. Zhang, D., Tsai, J.J.P.: Machine learning and software engineering. Softw. Qual. J. 11, 87–119 (2003). https://doi.org/10.1023/A:1023760326768

    Article  Google Scholar 

  18. Meinke, K., Bennaceur, A.: Machine learning for software engineering: models, methods, and applications. In: Proceedings of the 40th International Conference on Software Engineering: Companion Proceedings, pp. 548–549. ACM, New York, NY, USA (2018). https://doi.org/10.1145/3183440.3183461

  19. Garcia-Crespo, A., Colomo-Palacios, R., Gomez-Berbis, J.M., Mencke, M.: BMR: benchmarking metrics recommender for personnel issues in software development projects. Int. J. Comput. Intell. Syst. 2, 257–267 (2009)

    Google Scholar 

  20. Colomo-Palacios, R., González-Carrasco, I., López-Cuadrado, J.L., Trigo, A., Varajao, J.E.: I-Competere: Using applied intelligence in search of competency gaps in software project managers. Inf. Syst. Front. 16, 607–625 (2014). https://doi.org/10.1007/s10796-012-9369-6

    Article  Google Scholar 

  21. Colomo-Palacios, R., González-Carrasco, I., López-Cuadrado, J.L., García-Crespo, Á.: ReSySTER: a hybrid recommender system for Scrum team roles based on fuzzy and rough sets. Int. J. Appl. Math. Comput. Sci. 22, 801–816 (2012). https://doi.org/10.2478/v10006-012-0059-9

    Article  Google Scholar 

  22. The 1st International Workshop on Machine Learning and Software Engineering in Symbiosis (MASES 2018), https://mases18.github.io

  23. Khomh, F., Adams, B., Cheng, J., Fokaefs, M., Antoniol, G.: Software engineering for machine-learning applications: the road ahead. IEEE Softw. 35, 81–84 (2018). https://doi.org/10.1109/MS.2018.3571224

    Article  Google Scholar 

  24. Hevner, A., Chatterjee, S.: Design Research in Information Systems—Theory and Practice (2010)

    Google Scholar 

  25. Kitchenham, B.: Procedures for Performing Systematic Reviews. Keele UK Keele University, vol. 33, 2004 (2004)

    Google Scholar 

  26. Baldassarre, M.T., Caivano, D., Pino, F.J., Piattini, M., Visaggio, G.: Harmonization of ISO/IEC 9001:2000 and CMMI-DEV: from a theoretical comparison to a real case application. Softw. Qual. J. 20, 309–335 (2011). https://doi.org/10.1007/s11219-011-9154-7

    Article  Google Scholar 

  27. Glaser, B.G.: Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press (1978)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Østfold University College, 1757, Halden, Norway

    Ricardo Colomo-Palacios

Authors
  1. Ricardo Colomo-Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo Colomo-Palacios .

Editor information

Editors and Affiliations

  1. Research & Innovation Institute (Rii), Warsaw, Poland

    Anna Visvizi

  2. Deree College, The American College of Greece, Athens, Greece

    Miltiadis D. Lytras

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Colomo-Palacios, R. (2019). Towards a Software Engineering Framework for the Design, Construction and Deployment of Machine Learning-Based Solutions in Digitalization Processes. In: Visvizi, A., Lytras, M. (eds) Research & Innovation Forum 2019. RIIFORUM 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30809-4_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30809-4_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30808-7

  • Online ISBN: 978-3-030-30809-4

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation