Advertisement

Solution Patterns for Machine Learning

  • Soroosh NalchigarEmail author
  • Eric Yu
  • Yazan Obeidi
  • Sebastian Carbajales
  • John Green
  • Allen Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)

Abstract

Despite the hype around machine learning (ML), many organizations are struggling to derive business value from ML capabilities. Design patterns have long been used in software engineering to enhance design effectiveness and to speed up the development process. The contribution of this paper is two-fold. First, it introduces solution patterns as an explicit way of representing generic and well-proven ML designs for commonly-known and recurring business analytics problems. Second, it reports on the feasibility, expressiveness, and usefulness of solution patterns for ML, in collaboration with an industry partner. It provides a prototype architecture for supporting the use of solution patterns in real world scenarios. It presents a proof-of-concept implementation of the architecture and illustrates its feasibility. Findings from the collaboration suggest that solution patterns can have a positive impact on ML design and development efforts.

Keywords

Conceptual modeling Machine learning Advanced analytics Business analytics Design patterns 

References

  1. 1.
    Amazon SageMaker. http://aws.amazon.com/sagemaker/. Accessed 11 Mar 2018
  2. 2.
    Azure AI Gallery. http://gallery.azure.ai/. Accessed 11 Oct 2018
  3. 3.
    Azure Machine Learning Studio. http://azure.microsoft.com/en-us/services/machine-learning-studio/. Accessed 11 Mar 2018
  4. 4.
    Google Cloud AI products. http://cloud.google.com/products/ai/. Accessed 11 Mar 2018
  5. 5.
    Ali, R., Dalpiaz, F., Giorgini, P.: A goal-based framework for contextual requirements modeling and analysis. Requirements Eng. 15(4), 439–458 (2010)CrossRefGoogle Scholar
  6. 6.
    Breck, E., Cai, S., Nielsen, E., Salib, M., Sculley, D.: The ML test score: a rubric for ML production readiness and technical debt reduction. In: 2017 IEEE International Conference on Big Data, pp. 1123–1132. IEEE (2017)Google Scholar
  7. 7.
    Brynjolfsson, E., McAfee, A.: The business of artificial intelligence: what it can –and cannot– do for your organization. Harv. Bus. Rev. 7, 3–11 (2017)Google Scholar
  8. 8.
    Buschmann, F., Henney, K., Schimdt, D.: Pattern-Oriented Software Architecture, vol. 5. Wiley, Hoboken (2007)Google Scholar
  9. 9.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15 (2009)CrossRefGoogle Scholar
  10. 10.
    Chen, H.-M., Kazman, R., Haziyev, S.: Agile big data analytics for web-based systems: an architecture-centric approach. IEEE Trans. Big Data 2, 234–248 (2016)CrossRefGoogle Scholar
  11. 11.
    Chen, H.-M., Kazman, R., Haziyev, S., Hrytsay, O.: Big data system development: an embedded case study with a global outsourcing firm. In: Proceedings of the First International Workshop on BIG Data Software Engineering, pp. 44–50. IEEE Press (2015)Google Scholar
  12. 12.
    Davenport, T.H., Ronanki, R.: Artificial intelligence for the real world. Harv. Bus. Rev. 96(1), 108–116 (2018)Google Scholar
  13. 13.
    Goldstein, M., Uchida, S.: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS ONE 11(4), e0152173 (2016)CrossRefGoogle Scholar
  14. 14.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)zbMATHGoogle Scholar
  15. 15.
    Henke, N., et al.: The Age of Analytics: Competing in a Data-Driven World, vol. 4. McKinsey Global Institute, New York (2016)Google Scholar
  16. 16.
    Keet, C.M., et al.: The data mining optimization ontology. Web Seman. Sci. Serv. Agents World Wide Web 32, 43–53 (2015)CrossRefGoogle Scholar
  17. 17.
    Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. Informatica 31, 249–268 (2007)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Luca, M., Kleinberg, J., Mullainathan, S.: Algorithms need managers, too. Harv. Bus. Rev. 94(1), 20 (2016)Google Scholar
  19. 19.
    Nalchigar, S., Yu, E.: Conceptual modeling for business analytics: a framework and potential benefits. In: 19th IEEE Conference on Business Informatics, pp. 369–378 (2017)Google Scholar
  20. 20.
    Nalchigar, S., Yu, E.: Business-driven data analytics: a conceptual modeling framework. Data Knowl. Eng. 117, 359–372 (2018)CrossRefGoogle Scholar
  21. 21.
    Nalchigar, S., Yu, E.: Designing business analytics solutions: a model-driven approach. Bus. Inf. Syst. Eng. (2018)Google Scholar
  22. 22.
    Nalchigar, S., Yu, E., Ramani, R.: A conceptual modeling framework for business analytics. In: Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 35–49. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46397-1_3CrossRefGoogle Scholar
  23. 23.
    Ng, A.: What artificial intelligence can and can’t do right now. Harv. Bus. Rev. 9 (2016)Google Scholar
  24. 24.
    Ransbotham, S., Gerbert, P., Reeves, M., Kiron, D., Spira, M.: Artificial intelligence in business gets real. MIT Sloan Manag. Rev. (2018)Google Scholar
  25. 25.
    Schreck, B., Kanter, M., Veeramachaneni, K., Vohra, S., Prasad, R.: Getting value from machine learning isn’t about fancier algorithms – it’s about making it easier to use. Harv. Bus. Rev. (2018)Google Scholar
  26. 26.
    Sculley, D., et al.: Machine learning: the high interest credit card of technical debt. In: SE4ML: Software Engineering for Machine Learning (2014)Google Scholar
  27. 27.
    Vanschoren, J., Soldatova, L.: Exposé: an ontology for data mining experiments. In: International Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery, pp. 31–46 (2010)Google Scholar
  28. 28.
    Veeramachaneni, K.: Why you’re not getting value from your data science. Harv. Bus. Rev. 12, 1–4 (2016)Google Scholar
  29. 29.
    Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar
  30. 30.
    Yu, E.: Modelling strategic relationships for process reengineering. Soc. Model. Requirements Eng. 11, 2011 (2011)Google Scholar
  31. 31.
    Zinkevich, M.: Rules of machine learning: best practices for ML engineering (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Soroosh Nalchigar
    • 1
    Email author
  • Eric Yu
    • 1
  • Yazan Obeidi
    • 2
  • Sebastian Carbajales
    • 2
  • John Green
    • 2
  • Allen Chan
    • 2
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada
  2. 2.IBM Canada Ltd.MarkhamCanada

Personalised recommendations