This talk will begin with a brief retrospective on
advances in privacy-preserving data mining. We will
then look at legal and societal issues, and the
mismatches between these issues and the technology.
We will then look at some new research in the area,
including privacy metrics and techniques applicable
to data mining, and data/computation outsourcing.
Big data streams mark a new era in artificial intelligence and the data
mining literature. Video and voice streams have grown rapidly in recent
years. A single lab-based human-computer interaction experiment with one
human subject collecting Cognitive, Physiological, and other data can
easily generate a few terabytes of data in a single hour; growing
rapidly to a Petabyte within a timeframe less than a month. In an article in the Wired Magazine, 2008, by Chris Anderson, he wrote
"the data deluge makes the scientific method obsolete". He predicted
that in the age of Petabyte and beyond, a meaningful correlation
analysis is enough! Chris comment was provocative; but some started believing it. So was
Chris right or wrong? Why? What can we do to face the outburst of big
data? Do we have the data mining tools to manage these data? Where is
the future of data mining heading?
In this talk, I will discuss the above questions and demonstrate some
answers using examples of my work and analysis
Hussein Abbass is a Professor of Information Technology at the
University of New South Wales, Australian Defence Force Academy Campus
in Canberra. He is a Fellow of the Operational Research Society (UK), a
fellow of the Australian Computer Society, and a Senior Member of the
IEEE. He has more than 180 refereed papers and according to Microsoft
Academics, he is one of the top 0.3% most cited researchers worldwide in
Artificial Intelligence in the last 10 years.
Hussein is an Associate Editor of the IEEE Transactions on Evolutionary
Computation, the IEEE Computational Intelligence Magazine, the American
Institute of Mathematical Sciences Journal of Industrial and Management
Optimization, and the International Journal of Intelligent Computing and
Cybernetics.
Hussein's work integrates cognitive science, operations research and
artificial intelligence. He is the General Chair of the 2012 IEEE World
Congress on Computational Intelligence, to be held in Brisbane June
2012, and is the Premier and Largest event by the IEEE Computational
Intelligence Society that attracts close to 2000 researchers.
Dr Musa Mammadov, School of SITE, University of Ballarat, National ICT Australia
Drug-drug interactions: A Data Mining Approach
Drug-drug interaction is one of the important problems of Adverse Drug Reaction (ADR). This presentation describes a data mining approach to this problem developed at the University of Ballarat. This approach is based on drug-reaction relationships represented in the form of a vector of weights; each vector related to a particular drug can be considered as a pattern in causing adverse drug reactions. Optimal patterns for drugs are determined as a solution to some global optimization problem. Although this approach can be used for solving many ADR problems, we concentrate only on drug-drug interactions. The numerical implementations are carried out on different classes of reactions from the Australian Adverse Drug Reaction Advisory Committee (ADRAC) database. The results obtained extend our understanding of the drug-drug interaction from a data mining point of view.
Dr Musa Mammadov, born in Azerbaijan (former USSR), is a Senior Research Fellow at the School of Science, Information Technology and Engineering. He is a member of research team working on Large Scale Systems-Life Sciences Project at National ICT Australia (NICTA). He graduated from the faculty of Mechanics-Mathematics, Baku State University. He has PhD degrees in Mathematics (St-Petersburg State University, Russia) and in Mathematics and IT (University of Ballarat, Australia).
Dr Mammadov's key research interests are Optimal Control Theory, Global Optimization and Data Mining. He has published more than 100 research papers. Dr Mammadov's main contribution to the Optimal Control Theory related to the study of asymptotic behavior of optimal trajectories (the Turnpike Theory). His advances in this field have resulted in the development of powerful techniques for the stability analysis in continuous time control systems and have been applied to the study of a number of real world problems. He is the author of the global optimization method DSO, in the Global and Non-smooth Optimization Software (GANSO), released by the University of Ballarat (http://www.ganso.com.au). Data mining is another research area Dr Mammadov has been working on since joining to the University of Ballarat in 2000. In particular, his contribution to the study of Adverse-Drug Reaction (ADR) problems provided a new insight to these problems. He has developed a new approach to the Adverse-Drug Reaction problems by incorporating newly introduced non-linear data classification techniques and optimization models for corresponding problems. It allows studying many ADR problems, including the drug-drug interaction problem that attracts a huge interest nowadays.
