PhD School Tutorials

Prof. Alan David FEKETE, University of Sydney

Bio: Alan Fekete is Professor of Enterprise Software Systems within the School of Information Technologies at the University of Sydney. His undergraduate education was at the University of Sydney, and his doctorate was earned in the mathematics department of Harvard University. He has been recognized as a Distinguished Scientist by ACM, and he serves as Trustee for the VLDB Endowment. He is particularly known for a body of research on transaction management. He is also active in CS Education.

Title: Consistency Properties for Distributed Storage Platforms

Abstract: A scalable and fault-tolerant data storage layer is extremely useful when constructing scalable fault-tolerant application software. The application developer is a consumer of a service provided by the storage layer, and the interface between these parties needs to be precise. This tutorial reflects on several bodies of research that relate to understanding the implications for the consumer, of the consistency aspects of that interface. We cover in turn how consistency properties can be defined, how the consumer can measure consistency, and how to reason about applications when they must run over storage with consistency that is weaker-than-ideal.

Prof. Jeffrey Xu Yu, The Chinese University of Hong Kong

Bio: Dr Jeffrey Xu Yu is a Professor in the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. His current main research interests include graph mining, graph query processing, graph pattern matching, keywords search in databases, and online social networks. Dr. Yu served as an Information Director and a member in ACM SIGMOD executive committee (2007-2011), an associate editor of IEEE Transactions on Knowledge and Data Engineering (2004-2008), and an associate editor in VLDB Journal (2007-2013). Currently he servers as an associate editor of WWW Journal, the International Journal of Cooperative Information Systems, the Journal on Health Information Science and Systems (HISS), and Journal of Information Processing. Dr. Yu served/serves in many organization committees and program committees in international conferences/workshops including PC Co-chair of APWeb'04, WAIM'06, APWeb/WAIM'07, WISE'09, PAKDD'10, DASFAA'11, ICDM'12, NDBC'13, ADMA'14, and CIKM'15.

Title: Large Graph Processing: Algorithms and Systems

Abstract: The real applications that need graph processing techniques to handle a large graph can be found from many real applications including online social networks, biological networks, ontology, transportation networks, etc. In this talk, we will discuss some selected research topics on graph processing over large graphs from the algorithm perspectives and the systems perspectives.

Dr. Zi (Helen) Huang, The University of Queensland

Bio: Dr Zi Huang received her BSc degree from Tsinghua University, China, in 2001, and her PhD in Computer Science from the University of Queensland, Australia, in 2007. She is currently an ARC Future Fellow with the School of Information Technology and Electrical Engineering, University of Queensland. Her research interests include multimedia indexing and search, social data analysis and knowledge discovery.

Title: Social event and behavior modelling

Abstract: Social media data has provided great opportunities for many challenging data mining tasks. Its value has been widely exhibited in real-world applications. In this tutorial, we are focused on the impact of social media on public social event and individual online user behavior using heterogeneous social media data. We will review recent research advances in social event detection and prediction, and online user behavior modelling and prediction. As a step further, we will also discuss the effect of public events on individual behavior and explore their potential sequential correlations for new research opportunities.

Dr. Ying Zhang, University of Technology Sydney

Bio: Ying Zhang is a senior lecturer and ARC DECRA research fellow (2014-2016) at QCIS, the University of Technology Sydney (UTS). He received his BSc and MSc degrees in Computer Science from Peking University, China, and PhD in Computer Science from the University of New South Wales, Australia. His research interests include query processing on spatial data, spatial-textual data, streaming data, uncertain data and graphs. He has published 40+ papers on prestigious conferences and journals such as SIGMOD, SIGIR, VLDB, ICDE, TODS, VLDBJ, and TKDE. He was an Australian Research Council Australian Postdoctoral Fellowship (ARC APD) holder during 2010 and 2013.

Title: Querying and Mining of Geo-textual Data

Abstract: Proliferation of geo-position technologies (e.g., smart phones, general mobile devices and sensor networks) and online social media (e.g., Twitter, Foursquare and Facebook) has resulted in a huge flood of location data being integrated with various textual data (e.g., tweets and news), leading to the "geo-textual" data. The ever increasing amounts of geo-textual data have tremendous potential for the discovery of new and useful knowledge in many key applications such as location-based services (LBS), e-marketing and social networks. In this tutorial, we first highlight the importance of geo-textual data management and the unique challenges that need to be addressed. Subsequently, we provide an overview of the existing research on geo-textual data, covering modelling, ad-hoc spatial-keyword queries, continuous spatial-keyword queries, mining of geo-textual data and other relevant topics. Finally, we discuss the future research directions in this important and growing research area.

Dr. Kai (Alex) Qin, RMIT University

Bio: Dr Kai Qin received his BEng degree from Southeast University, China, in 2001, and his PhD from Nanyang Technological University, Singapore, in 2007. He is now a lecturer in Computer Science and Information Technology at RMIT University. His research interests include evolutionary computation, machine learning, image processing, GPU computing and service computing. He has published 60+ papers and received two best paper awards. Two of his co-authored papers are the 1st and 3rd most cited papers (Thomson Reuters) in IEEE Transactions on Evolutionary Computation (ERA A*) over the last 10 years. He is currently chairing the IEEE Computational Intelligence Society task force on collaborative learning and optimisation, promoting research on synergizing machine learning and intelligent optimisation techniques to resolve challenging real-world problems which involve learning and optimisation as indispensable and interwoven tasks.

Title: Data-Driven Evolutionary Optimisation

Abstract: Optimisation aims at finding the best solution from numerous feasible ones, which is demanded in nearly every field when resolving various problems arising therein. Evolutionary optimisation represents a family of optimisation techniques based on Darwinian principles, characterized by a population of candidate solutions which will be evolved via nature-inspired operators to search for the optimum. Intrinsically, it belongs to a generate-and-test problem solver which incrementally produces a large volume of "data" (i.e. candidate solutions) as search progresses with search experience encoded by such "data".
In the past few decades, a lot of efforts had been made to enhance evolutionary optimisation techniques via exploiting (e.g. using data analytics techniques) the "data" generated in the course of search. However, modern optimisation problems, featured with the fast-growing scale, complexity and uncertainty, can seldom be tackled by simply hybridizing evolutionary optimisation with some off-the-shelf data analytics techniques, and therefore call for an in-depth investigation on how to leverage the "data" generated during search to facilitate optimisation.
This tutorial aims to introduce a unified perspective on evolutionary optimisation techniques that adopts data analytics as an indispensable component, describe how to identify and address various data analytics tasks during the search process, and discuss an emerging research trend which makes use of search experience gained by solving some problems to facilitate solving other problems via knowledge transfer. The audience is expected to get to know the fundamentals and recent developments in data-driven evolutionary optimisation, and be inspired to employ such techniques to deal with their encountered optimisation problems.