Improving data exploration in graphs with fuzzy logic and large-scale visualisation
File(s)improving-data-exploration PREPRINT.pdf (5.57 MB)
Accepted version
Author(s)
Molina-Solana, MJ
Guo, Y
Birch, D
Type
Journal Article
Abstract
This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.
Date Issued
2017-01-03
Date Acceptance
2016-12-22
Citation
Applied Soft Computing, 2017, 53, pp.227-235
ISSN
1872-9681
Publisher
Elsevier
Start Page
227
End Page
235
Journal / Book Title
Applied Soft Computing
Volume
53
Copyright Statement
© 2017 Elsevier B.V. All rights reserved. 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/
Subjects
Graph sensemaking
Fuzzy logic
Data exploration
Large-scale visualisation
Graph visualisation
Publication Status
Published