Keynote and Plenary Speakers



The Hong Kong Polytechnic University, Hong Kong (香港理工大学)  

Biography: Dr. Cao is currently a Chair Professor of Department of Computing at The Hong Kong Polytechnic University, Hong Kong. He is also the director of the Internet and Mobile Computing Lab in the department and the director of University’s Research Facility in Big Data Analytics. His research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has co-authored 5 books, co-edited 9 books, and published over 500 papers in major international journals and conference proceedings. He received Best Paper Awards from conferences including DSAA’2017, IEEE SMARTCOMP 2016, ISPA 2013, IEEE WCNC 2011, etc.
He served the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society 2012-2014, a member of IEEE Fellows Evaluation Committee of the Computer Society and the Reliability Society, a member of IEEE Computer Society Education Awards Selection Committee, a member of IEEE Communications Society Awards Committee, and a member of Steering Committee of IEEE Transactions on Mobile Computing. Dr. Cao has served as chairs and members of organizing and technical committees of many international conferences, and as associate editor and member of the editorial boards of many international journals. Dr. Cao is a fellow of IEEE and ACM distinguished member. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.  

Title of Speech: When IoT Meets Big Data 

Abstract: As the Internet becomes increasingly ubiquitous, it is evolving into the medium for connecting objects that are embedded in the physical world. The coupling between such objects and a worldwide standard-based communication infrastructure constitutes the Internet of Things (IoT) and is characterized by machine-to-machine (M2M) communications. IoT has many applications including smart cities, logistics, industrial control and healthcare. Currently, IoT technologies still largely focus on the networking aspect of connecting and controlling the things. As the IoT continues to develop, further potential can be realized by a combination with related technology approaches such as Cloud computing, Big Data, and AI. In this talk, I will describe the evolution of IoT from instrumentation and interconnection to intelligence and summarize our research in the past years along this direction towards smart IoT. Smart IoT will facilitate a sustainable platform empowering advanced applications. I will focus on the current challenges and future development of smart IoT that adds intelligence to IoT leveraging big data analytics and edge computing. 



Prof. LI Qing (李青教授)

The Hong Kong Polytechnic University, Hong Kong (香港理工大学)  

Biography: Qing Li is currently a Chair Professor (Data Science) and the Head of the Department of Computing, the Hong Kong Polytechnic University. Formerly, he was the founding Director of the Multimedia software Engineering Research Centre (MERC), and a Professor at City University of Hong Kong where he worked in the Department of Computer Science from 1998 to 2018. Prior to these, he has also taught at the Hong Kong University of Science and Technology and the Australian National University (Canberra, Australia). Prof. Li served as a consultant to Microsoft Research Asia (Beijing, China), Motorola Global Computing and Telecommunications Division (Tianjin Regional Operations Center), and the Division of Information Technology, Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia. He has been an Adjunct Professor of the University of Science and Technology of China (USTC) and the Wuhan University, and a Guest Professor of the Hunan University (Changsha, China) where he got his BEng. degree from the Department of Computer Science in 1982. He is also a Guest Professor (Software Technology) of the Zhejiang University (Hangzhou, China) -- the leading university of the Zhejiang province where he was born. Prof. Li has been actively involved in the research community by serving as an associate editor and reviewer for technical journals, and as an organizer/co-organizer of numerous international conferences. Some recent conferences in which he is playing or has played major roles include APWeb-WAIM'18, ICDM 2018, WISE2017, ICDSC2016, DASFAA2015, U-Media2014, ER2013, RecSys2013, NDBC2012, ICMR2012, CoopIS2011, WAIM2010, DASFAA2010, APWeb-WAIM'09, ER'08, WISE'07, ICWL'06, HSI'05, WAIM'04, IDEAS'03,VLDB'02, PAKDD'01, IFIP 2.6 Working Conference on Database Semantics (DS-9), IDS'00, and WISE'00. In addition, he served as a programme committee member for over fifty international conferences (including VLDB, ICDE, WWW, DASFAA, ER, CIKM, CAiSE, CoopIS, and FODO). He is currently a Fellow of IET/IEE, a Senior Member of IEEE, a member of ACM-SIGMOD and IEEE Technical Committee on Data Engineering. He is the chairperson of the Hong Kong Web Society, and also served/is serving as an executive committee (EXCO) member of IEEE-Hong Kong Computer Chapter and ACM Hong Kong Chapter. In addition, he serves as a councilor of the Database Society of Chinese Computer Federation (CCF), a member of the Big Data Expert Committee of CCF, and is a Steering Committee member of DASFAA, ER, ICWL, UMEDIA, and WISE Society.  

Title of Speech: Event Modeling and Mining: a Plank Road Towards Explainable Events 

Abstract: Recently, research on event management has redrawn much attention and made great progress. As the core tasks of event management, event modeling and mining are essential for accessing and utilizing events effectively. In this talk, we provide a detailed review of event modeling and event mining. Based on a general definition, different characteristics of events are described, along with the associated challenges. Then, we define four forms of events in order to better classify currently available but somewhat confusing event types; we also compare different event representation and relationship analysis techniques used for different forms of events. We present Event Cube as an example event model which, devised to accommodate multi-sourced event discovery and multi-dimensional analysis of events, represents a big step forward towards explainable events. Finally, we discuss several pending issues and application-specific challenges which also shed light on future research directions. 



Prof. Hayato YAMANA

Waseda University, JAPAN (日本早稻田大学) 

Biography: Hayato YAMANA received his Dr. Eng. degree at Waseda University in 1993. He began his career at the Electrotechnical Laboratory (ETL) of the former Ministry of International Trade and Industry (MITI), and was seconded to MITI's Machinery and Information Industries Bureau for a year in 1996. He was subsequently appointed Associate Professor of Computer Science at Waseda University in 2000, and has been a professor since 2005. From 2003 to 2004, he was IEEE Computer Society Japan Chapter Chair. Since 2015, he has been director of IPSJ (Information Processing Society of Japan) and vice chairman of information and communication society of IEICE (the institute of electronics, information and communication engineers). At Waseda University, he has been deputy Deputy Chief Information Officer and WasedaX project director since 2015. His research area is big data analysis. Currently, his group engages in Japanese government funded project called “Secure Data Sharing and Distribution Platform for Integrated Big Data Utilization - Handling all data with encryption.” For more information, please refer to  

Title of Speech: Secure Computation in the Cloud 

Abstract: In this talk, I will pick up a privacy issue that effects to our society followed by introducing secure computation using fully homomorphic encryption (FHE) in cloud computing. IDC reported that “At least 40 percent of big data requires some level of security, from privacy protection to full-encryption.” Especially, enterprise data and personal information should be kept strictly secured. To handle such sensitive data, FHE is one of the key technology to realize secure computation, i.e., handling all data with encryption throughout the data life cycle. I will introduce the basic technologies and the application areas of FHE including our national projects. 



Prof. Hongwei Du

California State University, East Bay, USA (加州州立大学东湾校区)  

Biography: Professor Hongwei Du is the Coordinator of Information Technology Management Program in the College of Business and Economics at California State University, East Bay. He holds a Ph.D. in Operations Research from Florida Institute of Technology, a M.S. in Computer Science from Bowling Green State University and a M.S. in System Engineering from Beijing Institute of Automation. His works have been published in the California Journal of Operations Management, the European Journal of Information Systems, the International Journal of Innovation and Learning, the International Journal of Intercultural Information Management, the International Journal of Information and Decision Science, the International Journal of Electronic Healthcare, the Journal of Economic Studies, and the International Review of Business Research Papers.    

Title of Speech: The Power of Big Data –Starbucks’ Success (and Challenges) in China 

Abstract: Starbucks opened its first store in China in January 1999 with little fanfare and interest. Now 20 years later, China has become Starbucks' second largest and fastest growing market, with the expectation to eclipse the US market in the near future. Starbucks has already opened 3,600 stores in China, serving more than 6 million customers a week, in more than 150 cities, with a new store opening every 15 hours. The company has positioned itself as the premium coffee brand in China. Starbucks is now building more net new company-operated stores in China than in the US. Presently, Starbucks does not only rely on the quality of its coffee beans to satisfy its customers, the company also depends on data, both externally purchased and internally gathered, to improve its business. Tracing the history of Starbucks and its growth in China, my presentation continue on what the Big Data is in Starbucks, what it means to the company, how data is used to gain advantage, and how the company continues its incredible success today. Starbucks uses big data and artificial intelligence to make more accurate decisions and predictions on how their business operating, analyze factors such as street traffic or pedestrians flow to determine where its next stores should be, learn more about their customers, boost sales in the process, and increase their market share. Today, we can say that Starbucks brews every cup of coffee with beans plus strong data science and analytics. In addition, the presentation will discuss the developing challenges Starbucks are facing, from China’s home-grown competitors to impacts of the fluctuating China-US business environment and trade relationship. 


Prof. Simon Fong(方正天教授)

University of Macau, Macau SAR(澳门大学)  

Biography: Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng. Computer Systems degree and a PhD. Computer Science degree in 1993 and 1998 respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Australia and Asia. Dr. Fong has published over 432 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier (I.F. 3.5), IEEE IT Professional Magazine, (I.F. 1.661) and various special issues of SCIE-indexed journals. Simon is also an active researcher with leading positions such as Vice-chair of IEEE Computational Intelligence Society (CIS) Task Force on "Business Intelligence & Knowledge Management", and Vice-director of International Consortium for Optimization and Modelling in Science and Industry (iCOMSI). 

Title of Speech: Health Monitoring Systems Using Hybrid Sensing Devices 

Abstract: Kinect is a popular contactless IR-based 3D sensor that captures a person’s activity and movement data in real time. It is affordable, easy to deploy and offers rich computer programming possibilities for designing applications ranging widely from gaming to clinical uses. Given its capability to track and localize people, a full spectrum of data about movement and body physique can be gathered. In the past decade, the capability of such device has been exploited for improving healthcare. Intelligent Kinect applications have been developed for healthcare-related purposes such as rehabilitation, assisting people with impairments, Parkinson detection, and lifestyle interventions. Such healthcare related applications extend from the proximity of the person him/herself to localization and interaction with the environments and surrounding spaces. Some typical scenarios are elderly living monitoring, fall detection, accident avoidance, correcting sitting positions as well as scoliosis management, and smoking detection by gesture and posture detection. In this talk, prototypes of health monitoring system using the Kinect and other devices would be presented and demonstrated, showing the potential of applications from data collection, fusion and transformation to posture and gait recognition and human activity recognition. In particular, recent research momentums from the big data analytics aspect would be looked at. Fusing Kinect cameras with other auxiliary sensing devices for improving the overall efficacy is a hot research topic recently. The opportunities, challenges and future prospects would be discussed.