English
Bibtex
Cite as text
@Select Types{,
Journal = "Band-1",
Title= "Design Patterns based on Deep Learning analyzing Distributed Data",
Author= "Daniel Burkhardt, Nana Agyei-Kena, Patrick Frey, Sven Kurrle, Heiner Lasi",
Doi= "https://doi.org/10.30844/wi_2020_a5-burkhardt",
Abstract= "Data silos in many system landscapes complicate the creation of comprehensive information. Distributed Ledger Technology enables trust between assets via a distributed, secure and immutable storage of transactions. Deep Learning realizes intelligence to make decisions, conduct them and analyze their results based on the gathered data. In order to counteract the limitations of current system landscapes, an integrative implication of both Distributed Ledger Technology and Deep Learning is needed. Many considerations arise during the design of information systems integrating the named technologies. Transparency over the training and deployment of various Deep Learning methods on a distributed data landscape needs to be achieved. Using both a literature review and a qualitative research approach, this paper describes the development of design patterns and their selection criteria with different dimensions taken into consideration. The evaluation phase comprises of semi-structured interviews with experts from different disciplines. The result of this paper guides stakeholders in the selection of a suitable technical solution.
",
Keywords= "Deep Learning, Distributed Data, Design Patterns, Distributed Ledger Technology",
}
Daniel Burkhardt, Nana Agyei-Kena, Patrick Frey, Sven Kurrle, Heiner Lasi: Design Patterns based on Deep Learning analyzing Distributed Data. Online: https://doi.org/10.30844/wi_2020_a5-burkhardt (Abgerufen 18.04.24)
Open Access
Abstract
Abstract
Data silos in many system landscapes complicate the creation of comprehensive information. Distributed Ledger Technology enables trust between assets via a distributed, secure and immutable storage of transactions. Deep Learning realizes intelligence to make decisions, conduct them and analyze their results based on the gathered data. In order to counteract the limitations of current system landscapes, an integrative implication of both Distributed Ledger Technology and Deep Learning is needed. Many considerations arise during the design of information systems integrating the named technologies. Transparency over the training and deployment of various Deep Learning methods on a distributed data landscape needs to be achieved. Using both a literature review and a qualitative research approach, this paper describes the development of design patterns and their selection criteria with different dimensions taken into consideration. The evaluation phase comprises of semi-structured interviews with experts from different disciplines. The result of this paper guides stakeholders in the selection of a suitable technical solution.
Keywords
Schlüsselwörter
Deep Learning, Distributed Data, Design Patterns, Distributed Ledger Technology
References
Referenzen
1. Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics 19, 1236–1246 (2018)
2. Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT Press, Cambridge, Massachusetts, London, England (2016)
3. Erbay, H., Yıldırım, N.: Technology Selection for Digital Transformation: A Mixed Decision Making Model of AHP and QFD. In: Durakbasa, N.M., Gencyilmaz, M.G. (eds.) Proceedings of the International Symposium for Production Research 2018, 104, pp. 480– 493. Springer International Publishing, Cham (2019)
4. Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Quarterly, 75–105 (2004)
5. Mehta, N., Pandit, A.: Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics 114, 57–65 (2018)
6. Gamma, E., Vlissides, J., Johnson, R., Helm, R.: Design patterns. Elements of reusable object-oriented software. Addison-Wesley, Boston (1998)
7. Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review 65, 386 (1958)
8. Yanling, Z., Bimin, D., Zhanrong, W.: Analysis and study of perceptron to solve XOR problem. In: Proceedings, pp. 168–173. IEEE, [Piscataway, NJ] (2002)
9. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition (2015)
10. Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3– 18 (2017)
11. LeCun, Y.: A theoretical framework for back-propagation. In: Proceedings of the 1988 Connectionist Models Summer School, CMU, Pittsburg, PA (1988)
12. Buterin, V.: The Meaning of Decentralization, https://medium.com/@VitalikButerin/themeaning- of-decentralization-a0c92b76a274
13. Protocol Labs: IPFS Documentation, https://docs.ipfs.io/
14. Wood, G.: Ethereum: A secure decentralised generalised transaction ledger. Ethereum project yellow paper 151, 1–32 (2014)
15. Burkhardt, D., Werling, M., Lasi, H.: Distributed Ledger. Definition & Demarcation. In: Conference proceedings ICE/IEEE ITMC. IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1–9. IEEE, Piscataway, NJ (2018)
16. Li, M., Andersen, D.G., Park, J.W., Smola, A.J., Ahmed, A., Josifovski, V., Long, J., Shekita, E.J., Su, B.-Y.: Scaling Distributed Machine Learning with the Parameter Server. In: 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14), pp. 583–598. USENIX Association, Broomfield, CO (2014)
17. McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication- Efficient Learning of Deep Networks from Decentralized Data (2017)
18. Yan Liu, Jimeng Sun: Deep Learning Models for Health Care: Challenges and Solutions. Complex Intell. Syst. (2017)
19. Lin, S.W., Miller, B., Durand, J., Bleakley, G., Chigani, A., Martin, R., Murphy, B., Crawford, M.: The Industrial Internet of Things Volume G1: Reference Architecture. IIC:PUB: G1:V1.80:20170131 (2017)
20. Reinfurt, L., Breitenbücher, U., Falkenthal, M., Leymann, F., Riegg, A.: Internet of things patterns. In: Eloranta, V.-P. (ed.) Proceedings of the 21st European Conference on Pattern Languages of Programs, pp. 1–21. ACM, New York, NY (2016)
21. Qanbari, S., Pezeshki, S., Raisi, R., Mahdizadeh, S., Rahimzadeh, R., Behinaein, N., Mahmoudi, F., Ayoubzadeh, S., Fazlali, P., Roshani, K., et al.: IoT Design Patterns: Computational Constructs to Design, Build and Engineer Edge Applications. In: IEEE First International Conference on Internet-of-Things Design and Implementation, pp. 277–282. Piscataway, NJ (2016)
22. Mendis, G., Sabounchi, M., Wei, J. and Roche, R.: Blockchain as a Service: An Autonomous, Privacy Preserving, Decentralized Architecture for Deep Learning (2018)
23. Kuo, T.-T., Ohno-Machado, L.: ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks (2018)
24. Hevner, A., Chatterjee, S.: Design Research in Information Systems. Theory and Practice. Springer Science+Business Media LLC, Boston, MA (2010)
25. Sarker, S., Xiao, X., Beaulieu, T.: Guest editorial: qualitative studies in information systems: a critical review and some guiding principles. MIS Quarterly 37, iii–xviii (2013)
26. Österle, H., Becker, J., Frank, U., Hess, T., Karagiannis, D., Krcmar, H., Loos, P., Mertens, P., Oberweis, A., Sinz, E.J.: Memorandum on design-oriented information systems research. European Journal of Information Systems 20, 7–10 (2011)
27. Peffers, K., Tuunanen, T., Niehaves, B.: Design science research genres: introduction to the special issue on exemplars and criteria for applicable design science research. European Journal of Information Systems 27, 129–139 (2018)
28. Myers, M.D., Newman, M.: The qualitative interview in IS research: Examining the craft. Information and Organization 17, 2–26 (2007)
29. Mayring, P., Fenzl, T.: Qualitative content analysis: theoretical foundation, basic procedures and software solution. In: Baur, N., Blasius, J. (eds.) Handbuch Methoden der empirischen Sozialforschung, 3, pp. 633–648. Springer Fachmedien Wiesbaden, Wiesbaden (2019)
30. Kaggle Inc.: Kaggle is the place to do data science projects, https://www.kaggle.com/
31. Peteiro-Barral, D., Guijarro-Berdiñas, B.: A survey of methods for distributed machine learning. Prog Artif Intell 2, 1–11 (2013)