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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Employing Machine Learning in the Policy-based Blockchain Selection Process |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2021 |
| Abstract Text | In recent years the blockchain topic, the underlying technology of cryptocurrencies, has gained increasing importance and attention. Since the invention of Bitcoin in 2009, the number of cryptocurrencies and various blockchain platforms has drastically increased. With such a myriad of implementations and the resulting lack of transparency, it become complex to select a suitable blockchain for a specific use case. Recently, a policy-based management approach has been proposed to automate the selection process, which recommends blockchain implementations based on transaction information and pre-defi ned policies. This selection process is governed by a simple algorithm, which applies straightforward filtering. The goal of this thesis is the research of novel approaches, such as machine learning, and apply them to the blockchain selection process with the aim of integrating them into the existing solution. Therefore, various machine learning algorithms were trained, deployed and evaluated on their applicability in the blockchain selection process. The developed prototype extends the existing solution and provides users the option to choose between the conventional and the machine learning-based selection algorithm, with changing policy parameters depending on the selection. The results of the evaluation show that such a combination is in fact feasible and can deliver accurate results. But it also highlights pitfalls and necessary considerations. |
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