We are proud that BDVA’17 will host several internationally renowned keynote speakers.
Associate Professor, Monash University
Tim received his PhD on “Two and a Half Dimensional Visualisation of Relational Networks” from the University of Sydney in 2005. He was a post-doctoral Research Fellow at Monash University from 2005 to 2008, then a Visiting Researcher at Microsoft Research, USA in 2008-2009. From 2009 to 2012 he worked as a Senior Software Development Engineer with the Visual Studio product group at Microsoft, USA. A highlight of this period was shipping the Code Map software dependency visualisation tool with Visual Studio 2012. In late 2012 he returned to Monash University as a Larkins Fellow where he now co-directs the Immersive Analytics Initiative and is a founding member of the Monash Adaptive Visualisation Lab.
Immersive analytics: Interactive data analysis using the surfaces and spaces around us
Humans struggle to understand the masses of complex data they now accumulate. Visual data analytics offers a solution, and we are exploring the potential for new immersive display and interaction technologies to greatly enhance this potential. Immersive Analytics is a new research field developing the first practical and theoretical frameworks for immersive data analysis. Our work is informed by controlled studies and systematic design exploration; and user-centred design of practical tools for immersive data analytics. Findings that lead to more effective, engaging and collaborative systems for data analytics will ultimately allow people to make more informed decisions from data.
3D Qld and the Digital Built Environment
In early 2016, a combined industry-government Taskforce in Queensland appointed a team led by ACIL Allen Consulting in conjunction with VANZI to prepare the Road Map for 3D Qld to create the Digital Built Environment (DBE). It will be formed by linking millions of individual 3D models under the control of millions of different entities into: a federated authorised secure 3D computer model of the natural and built environment (inside and out, above and below ground) on all scales required for decision-making.The DBE will also be tied to the global position and elevation grids to ensure spatial integrity.
Crucial to the DBE is the integration (within the model) of an accurate 3D Cadastre (and other rights boundaries, eg lease) to facilitate:
1. location of boundaries on the ground and inside buildings using the model for reference
2. decision-making in the model, and
3. privacy and security by linking access to parts of the federated model based on each person’s identity, role and permissions tied to their rights in the real world.
In time, it is expected that every bit of information about any object will be linked to its digital twin, so that it can be accessed by merely ‘pointing’ at the object in VR or AR – subject to permission.
The team has undertaken extensive interviews with stakeholders across all sectors: local and state government, utilities, surveying, architecture, engineering, costing, finance, fabrication, construction, fit-out, lighting, painting, decorating, landscaping, insurance, asset and facility management, leasing, valuation, sale and decommission; through to agriculture, transport, emergency services and disaster recovery, as well as many hardware and software providers that offer the capability to create 3D computer models of the real world.
From these interviews, it is evident that there are no technical impediments to the creation of the DBE. Research has also identified conservative $2+ bn of benefits to the Qld economy ($15 bn for Australia) – once fully implemented.
Professor, University of South Australia
Dr Jiuyong Li is a Professor and an Associate Head of School at the School of Information
Technology and Mathematical Sciences of the University of South Australia. He leads the Data Analytics
Group in the School. His main research interests are in data mining, bioinformatics, and data
privacy. He has led six Australian Research Council Discovery projects and one Data to Decision
Cooperative Research Centre project. He has published more than 100 papers, mostly in leading
journals and conferences in the areas. He has been a chair (or a PC chair) of multiple Australasian
data mining and artificial intelligence conferences and actively serving PC (and senior PC) member
for many international conferences in data mining. He has received senior visiting fellowships from
Nokia Foundation, the Australian Academy of Science, and Japan Society of Promotion of Science.
Exploring causal relationships in data
Identifying relationships among variables is important in understanding data and making
decisions. Associations are the most widely explored relationships in data. An association
shows that two variables exhibit the same (or opposite) trend but may not indicate that the
two variables have an inherent relationship. One major problem for exploring relationships in
a data set with many variables is that there will be so many associations which overwhelm the
true and inherent relationships. Causal relationship discovery is to find the inherent
relationships where the change of one variable leads to the change of another. The
identification of causal relationships is crucial for understanding data and supports evidence
based decision making. Causal discovery is a central task for science, health, economy and
nearly all areas of studies. In this talk, I will discuss the current progress in the area and
some of our work.