Improving Data Exploration in Graphs with Fuzzy Logic and Large-Scale Visualisation

Published in Applied Soft Computing, vol 53, pp 227-235, 2017

Recommended citation: Molina-Solana, M., Birch, D., Guo, Y. (2017), "Improving Data Exploration in Graphs with Fuzzy Logic and Large-Scale Visualisation", Applied Soft Computing Vol. 53, pp. 227-235. http://doi.org/10.1016/j.asoc.2016.12.044

Another work on the benefits of employing visualisation for data sensemaking.

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Recommended citation: Molina-Solana, M., Birch, D., Guo, Y. (2017), "Improving Data Exploration in Graphs with Fuzzy Logic and Large-Scale Visualisation", Applied Soft Computing Vol. 53, pp. 227-235.

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.

BibTeX: @article{Molina-Solana2017b, author = {Miguel Molina-Solana and David Birch and Yike Guo}, title = {Improving Data Exploration in Graphs with Fuzzy Logic and Large-Scale Visualisation}, journal = {Applied Soft Computing}, year = {2017}, volume = {53}, pages = {227--235}, doi = {http://doi.org/10.1016/j.asoc.2016.12.044} }