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A Controlled Natural Language to Support Intent-based Blockchain Selection


John Scheid, Eder; Widmer, Patrick; Rodrigues, Bruno; Figueredo Franco, Muriel; Stiller, Burkhard (2020). A Controlled Natural Language to Support Intent-based Blockchain Selection. In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC 2020), Toronto, Canada, 3 May 2020 - 6 May 2020, 1-9.

Abstract

In the last years, cryptocurrencies have becomeincreasingly popular along with their underlying distributedledger technology, referred to as a Blockchain (BC). Nowadays,a wide variety of BC implementations are available. However,the selection of a suitable implementation for a particularapplication or use case is complex because it requires technicalunderstanding of the underlying BC implementation aspects.Therefore, this paper proposes a Controlled Natural Language(CNL) to extends existing BC selection solutions to abstractunderlying implementation details. The approach allows thespecification abstract high-level policies, referred to as intents, inan English-based language. The approach is inspired by previousapproaches from the network management field. Moreover, astate machine-based refinement technique is proposed to refinethese intents into low-level BC selection policies. The resultsof the performance evaluation of the prototype implementationshow that the refinement process presents a minimal overhead.In addition, the perceived intuitiveness of the CNL by userswas assessed in a survey. The results of the survey suggest thattechnical and non-technical individuals benefit from an intent-based approach equally

Abstract

In the last years, cryptocurrencies have becomeincreasingly popular along with their underlying distributedledger technology, referred to as a Blockchain (BC). Nowadays,a wide variety of BC implementations are available. However,the selection of a suitable implementation for a particularapplication or use case is complex because it requires technicalunderstanding of the underlying BC implementation aspects.Therefore, this paper proposes a Controlled Natural Language(CNL) to extends existing BC selection solutions to abstractunderlying implementation details. The approach allows thespecification abstract high-level policies, referred to as intents, inan English-based language. The approach is inspired by previousapproaches from the network management field. Moreover, astate machine-based refinement technique is proposed to refinethese intents into low-level BC selection policies. The resultsof the performance evaluation of the prototype implementationshow that the refinement process presents a minimal overhead.In addition, the perceived intuitiveness of the CNL by userswas assessed in a survey. The results of the survey suggest thattechnical and non-technical individuals benefit from an intent-based approach equally

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:6 May 2020
Deposited On:22 Jan 2021 07:26
Last Modified:23 Jan 2021 01:00
Publisher:IEEE
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://files.ifi.uzh.ch/CSG/staff/scheid/extern/publications/ICBC2020-CNL.pdf
Other Identification Number:merlin-id:20504

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