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Visualisation

Computing the visualisation of big complex data
We're developing new visual representation and visualisation methods for humans to find patterns in complex and abstract data sets.

Technological advances such as sensors have increased data volumes to the extent that we are now experiencing a “data deluge”, in which data is produced faster than it can be used by humans. 

Our research aims to develop new visual representation, visualisation and interaction methods for humans to find patterns in huge abstract data sets, especially network data sets.

These data sets include social networks, telephone call networks, biological networks, physical computer networks, stock buy-sell networks, and transport networks.

These new visualisation and interaction methods are in high demand by industry, and new algorithms will be used in the next generation visual analytic tools to enable analysts develop new insights and new knowledge of big complex data.

Visualisation connections graph

Our experts: Professor Seokhee Hong, Professor Peter Eades

Our partner: Professor Kwan-Liu Ma (UC Davis, USA)

Funding: Australian Research Council Discovery Grant (2018)

We're designing new sublinear algorithms for the visual analytics of extreme-scale networks, involving billions of nodes.

Based on algorithmics for graph drawing, integrating sublinear algorithms and distributed algorithms, our project will introduce new quality metrics for good visualisation of extreme-scale networks, design new sublinear-time algorithms to compute good visualisation, implement them in a distributed computing environment, and evaluate with a real world social network and biological network data sets.

The new algorithms produced by this project will be used in the next generation visual analytic tools for extreme-scale data to enable analysts develop new insights and new knowledge of extreme-scale data.

Our experts: Professor Seokhee Hong, Professor Peter Eades

Our partner: Professor Daniel Keim (University of Konstanz, Germany)

Funding: Australian Research Council Discovery Grant (2019)

We aim to deliver new models, metrics and algorithms for Faithful Visual Analytics of complex data. For a purported visual representation of some data, "faithfulness" measures how accurately the visual representation describes the data.

We will develop new models for Faithful Visual Analytics, design new faithfulness metrics for faithful visual analytics of complex networks, design new algorithms to compute faithful visualisations, and evaluate using real world social network and biological network data sets.

The new models, metrics and algorithms produced by this project will be used in the next generation Visual Analytic tools to enable analysts develop accurate insights and new knowledge of complex data.

Our expert: Professor Seokhee Hong, Professor Peter Eades

Our partner: Oracle Research Lab

Funding: Australian Research Council Linkage Grant (2017)

Recent years have seen an explosion in the amount of network data available: software systems, social networks and biological systems have millions of nodes and billions of edges.

The data is complex, as nodes and edges have multivariate attributes. Exploiting such data sets is hard due to its size and complexity – using current methods, a normal person cannot even deal with a fraction of the data. More efficient ways to understand the data are needed. 

We propose to design, implement and evaluate new visualisation methods that scale well with the size of today’s and future (larger) data sets. In common language, a picture is worth a thousand words.

We aim to create such pictures for massive multivariate network data sets. Our project will create new methods for the visual analysis of massive multivariate networks such as complex software systems, social networks, and biological systems.

The results of our project will be used by industry companies in software development, biotechnology, and security, to exploit of their data, by enabling new visualisation methods that allow humans to understand the data.