Knowledge-driven Exploratory Performance Data Analysis for Execute-Order-Validate Blockchains
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Date
2023
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Knowledge-driven Exploratory Performance Data Analysis for Execute-Order-Validate Blockchains
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Authors
Al-Gburi, Noor
Kocsis, Imre
Kocsis, Imre
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en
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könyvfejezet
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Open access
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Szerző
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2023.02.06-2023.02.07.
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Budapest
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30th Minisymposium of the Department of Measurement and Information Systems
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978-963-421-904-0
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Proceedings of the 30th Minisymposium
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Department of Measurement and Information Systems
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Post print
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Faculty of Electrical Engineering and Informatics
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37
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ontology-based approach
hyperledger fabric
knowledge-based
exploratory data analysis
hyperledger fabric
knowledge-based
exploratory data analysis
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Konferenciacikk
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Budapest University of Technology and Economics
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Abstract
Exploratory data analysis (EDA) of the performance characteristics of complex IT systems, such as enterprise blockchain solutions, would significantly benefit from explicit representations of, and inference on knowledge about the analyzed system. However, connecting EDA and knowledge representation is not part of the current practice. As a novel approach, this paper presents a generic hierarchical activity ontology, connected to Hyperledger Fabric experiments with end-to-end delay, endorsement delay, ordering delay, and block validation observations. On this basis, we present rules for inferring knowledge-based visualization declarations on this ontology. Lastly, we generate Jupyter notebooks for the inferred sequence of visualizations.