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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Characterization and Classifications of Blockchains using Softgoal Interdependency Graphs and Machine Learning |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2021 |
| Abstract Text | With the emergence of several thousand Blockchains in recent years, the selection of use-case appropriate Blockchains has become a formidable task. Withal, hitherto no Blockchain standardization body is prevalent, leading to a research gap concerning the characterization and classification of Blockchains. Therefore, this thesis' approach to bridge this gap is by taking inspiration from software engineering principles, namely Functional and Non-Functional Requirements and their characterization using Softgoal Interdependency Graphs. Through decompositions and quantification of relationships, these graphs allow the understanding of how certain Blockchain attributes and aspects are achieved and obtained, and they facilitate the comparison of various Blockchain implementations. Consequently, a Blockchain specific Softgoal Interdependency Graph was designed and evaluated on two relevant use cases. Due to the inherent structure of the graph, a Blockchain characterization relating to four different attributes was achieved, and a Machine Learning evaluation on the use cases resulted in a classification of three different Blockchain clusters. Therefore, this approach can be deemed as successful, and future work can utilize this process and the resulting values to incorporate them into the Blockchain Selection task. |
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