2014第二届企业系统国际会议（ICES 2014)

Gang Li 
PhD
Director of TULIP Lab
Deakin University
Australia

Date: August 2, 2014
Title: Ride the wave, but not drown your enterprise in Big Data 

Abstract 
Big data is a double-edged sword: leveraging it will drive competitive advantage; ignoring it will risk your enterprise of lagging behind the competition. For enterprises, two potential threats from Big Data are identified: the first one is from the enterprise data released to public, which are subjected to legal requirements for privacy before they can be made available to other parties; the second one is from the data about the enterprise on social networks, which have a strong influence on a brand reputation that most enterprises should not ignore. 

For the first threat from the collected data, as most collected information contains private or sensitive information, how to release them while preserving privacy is an important issue that need to be addressed. Recently, the notion of differential privacy has been successfully used in industries and academic communities. Differential privacy acquires the intuition that releasing an aggregated report should not reveal too much information on any individual record in the dataset. This can be achieved using randomized mechanisms whose output distribution remains almost unchanged even with an arbitrary individual record deleted. A newly proposed private tagging system will be presented to illustrate the mechanism of differential privacy. 

For the second threat from social network data, it has been found that online astroturfers have been hired to post product comments on different social networks. They can also act maliciously by spreading negative or false information about competitors. For an enterprise, a serious and important research issue is to identify the abnormal contents on social networks and collect the evidence of astroturfersbehaviors. It is possible to identify those online astroturfers through the content analysis and the user behavior analysis. More specially, with the understanding of opinion spreading model, the content analysis will find similar contents, locating suspected users for further processing; the user behavior analysis will decide whether an unknown behavior belongs to an astroturferbehavior by training from known samples. To be hidden properly, an astroturfer usually changes its behaviors on different social Websites. For this case, we focus on the adaptability and dynamic of the detecting approach. The unsupervised deep learning can be utilized for dynamical feature extraction and selection, and multi-task learning can be exploited to handle different behavior context for better feature engineering.

Short Bio 
Gang Li, Director of TULIP Lab, Senior Lecturer, Deakin University (Australia), IEEE Senior Member. He received his PhD in computer science in 2005, and joined the School of Information Technology at Deakin University (Australia) as an associate lecturer in 2004. His research lab is working in the area of data mining, machine learning, behavior informatics and business intelligence. 

He is an associate editor for Decision Support Systems (Elsevier), and has been the guest editor for the Chinese Journal of Computer, Journal of Networks, and Future Generation Computer Systems (Elsevier). He has co-authored four papers that won best paper prizes, including ACM/IEEE ASONAM 2012 best paper and Springer’s 2007 Nightingale prize. He served on the program committee for over 80 international conferences in artificial intelligence, data mining and machine learning, tourism and hospitality management.

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