Name
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 modeling 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.future.2018.06.015 | Future Generation Computer Systems |
Keywords | Field | DocType |
Graphs,Visualization,Big data,Linked data,Performance analysis | Graph,Knowledge graph,Architecture,Spark (mathematics),Computer science,Visualization,Semantic Web,Implementation,Theoretical computer science,Big data,Distributed computing | Journal |
Volume | ISSN | Citations |
89 | 0167-739X | 1 |
PageRank | References | Authors |
0.35 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Gómez-Romero | 1 | 404 | 21.69 |
| Miguel Molina-Solana | 2 | 48 | 12.80 |
| Axel Oehmichen | 3 | 25 | 2.88 |
| Yike Guo | 4 | 1319 | 165.32 |