Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Gomez-Romero:2018:10.1016/j.future.2018.06.015,
author = {Gomez-Romero, J and Molina-Solana, MJ and Oehmichen, A and Guo, Y},
doi = {10.1016/j.future.2018.06.015},
journal = {Future Generation Computer Systems},
pages = {224--238},
title = {Visualizing large knowledge graphs: a performance analysis},
url = {http://dx.doi.org/10.1016/j.future.2018.06.015},
volume = {89},
year = {2018}
}
TY - JOUR
AB - 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.
AU - Gomez-Romero,J
AU - Molina-Solana,MJ
AU - Oehmichen,A
AU - Guo,Y
DO - 10.1016/j.future.2018.06.015
EP - 238
PY - 2018///
SN - 0167-739X
SP - 224
TI - Visualizing large knowledge graphs: a performance analysis
T2 - Future Generation Computer Systems
UR - http://dx.doi.org/10.1016/j.future.2018.06.015
UR - http://hdl.handle.net/10044/1/61263
VL - 89
ER -