Invited Talk

Title: Mining Patterns in Social Media - A New Frontier
Speaker: Prof Huan Liu
               Ira A. Fulton Schools of Engineering,

               Arizona State University

Abstract:

In the era of social media, we are presented with new opportunities and unparalleled challenges. In this talk, we will introduce some of our recent studies on mining patterns hidden in social media. We inquire whether one can disentangle the complicated connections among users to find their intrinsic group memberships; look into user migration patterns in the presence of seemingly unlimited choices of social media services; investigate how to protect user privacy in social networking, and question the comparability between what we learn about social behavior in social media and what we can expect in the physical world. The study of pattern mining in social media can help understand emerging social behavior, and develop social media applications and services that offer better user experience.

Short Bio:

Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. He is recognized for excellence in teaching and research in Computer Science and Engineering at Arizona State University. His research interests are data mining, machine learning, social computing, and artificial intelligence. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He serves on journal editorial boards and numerous conference program committees, and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction (http://sbp.asu.edu/). For contact information and links to recent publications, please visit http://www.public.asu.edu/~huanliu/.

 

Title:Towards Context Aware On-demand Data Mining
Speaker:
Prof Jian Pei

               School of Computing Science
               Simon Fraser University

Abstract:

How can we make an application smarter?  The fast increasing availability of user data and the latest advances in data mining technology provide a unique opportunity to produce data driven applications smarter than ever before.  The critical trick is to develop context-aware on-demand data mining techniques, and integrate them into every step of applications.  In this talk, I will discuss some important building blocks for context-aware on-demand data mining, and use our experience in building a few search engine scale, data driven applications to illustrate the opportunities and the challenges.

Short Bio:

Jian Pei is currently Professor of Computing Science at the School of Computing Science at Simon Fraser University, Canada.   His is interested in researching and developing effective and efficient data analysis techniques for novel data intensive applications. Particularly, he is currently working on various techniques of data mining, Web search, information retrieval, data warehousing, online analytical processing, and database systems, as well as their applications in social networks, health-informatics, business and bioinformatics. His research has been extensively supported in part by governmental funding agencies and industry partners.  He is also active in developing industry relations and collaboration, transferring technologies developed in his group to industry applications, and developing proof-of-concept prototypes. Since 2000, he has published 1 textbook, 2 monographs and over 170 research papers in refereed journals and conferences, which have been cited thousands of times.  He has served in the organization committees and the program committees of over 160 international conferences and workshops.  He is the associate editor-in-chief of IEEE Transactions of Knowledge and Data Engineering (TKDE), and an associate editor or editorial board member of the premier academic journals in his fields, including ACM Transactions on Knowledge Discovery from Data (TKDD), Data Mining and Knowledge Discovery, Knowledge and Information Systems, Statistical Analysis and Data Mining, Intelligent Data Analysis, and Journal of Computer Science and Technology. He is a senior member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and an ACM Distinguished Speaker.   He is the recipient of several prestigious awards.

  




 

Highlight & News

[Last updated: Tue May 24 2011]

  • DDDM2011 accepted by ICDM2011.

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