Visualizing large knowledge graphs: a performance analysis
File(s)
Accepted version
Author(s)
Gomez-Romero, Juan
Molina-Solana, MJ
Oehmichen, Axel
Guo, Yike
Type
Journal Article
Abstract
Knowledge graphs are an increasingly important source of data and context information in Data Science. A first step in data analysis is data exploration, in which visualization plays a key role. Currently, Semantic Web technologies are prevalent for modelling and querying knowledge graphs; however, most visualization approaches in this area tend to be overly simplified and targeted to small-sized representations. In this work, we describe and evaluate the performance of a Big Data architecture applied to large-scale knowledge graph visualization. To do so, we have implemented a graph processing pipeline in the Apache Spark framework and carried out several experiments with real-world and synthetic graphs. We show that distributed implementations of the graph building, metric calculation and layout stages can efficiently manage very large graphs, even without applying partitioning or incremental processing strategies.
Date Issued
2018-12-01
Date Acceptance
2018-06-10
Citation
Future Generation Computer Systems, 2018, 89, pp.224-238
ISSN
0167-739X
Publisher
Elsevier
Start Page
224
End Page
238
Journal / Book Title
Future Generation Computer Systems
Volume
89
Copyright Statement
© 2018 Elsevier B.V. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
European Commission
Grant Number
GA 743623
Subjects
graphs
visualization
big data
linked data
performance analysis
Publication Status
Published
Date Publish Online
2018-06-30