Knowledge-driven Exploratory Performance Data Analysis for Execute-Order-Validate Blockchains
Date
Authors
Type
könyvfejezet
Language
en
Reading access rights:
Open access
Rights Holder
Szerző
Conference Date
2023.02.06-2023.02.07.
Conference Place
Budapest
Conference Title
30th Minisymposium of the Department of Measurement and Information Systems
ISBN, e-ISBN
978-963-421-904-0
Container Title
Proceedings of the 30th Minisymposium
Department
Department of Measurement and Information Systems
Version
Post print
Faculty
Faculty of Electrical Engineering and Informatics
First Page
37
Subject (OSZKAR)
ontology-based approach
hyperledger fabric
knowledge-based
exploratory data analysis
hyperledger fabric
knowledge-based
exploratory data analysis
Gender
Konferenciacikk
University
Budapest University of Technology and Economics
- Cite this item
- https://doi.org/10.3311/minisy2023-010
OOC works
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.