Tutorial Program

23nd International Conference on Information and Knowledge Management (CIKM)
Shanghai, China. Nov 3-7, 2014


We are happy to announce that the following tutorials will be given at ACM CIKM 2014. 

Schedule Tutorial Title Tutorial Presenters
Tutorial 1
Nov 3, 2014
8:30-12:00am
Crowdsourcing in Information and Knowledge Management
Lei Chen
Dongwon Lee
Meihui Zhang
Tutorial 2
Nov 3, 2014
8:30-12:00am
Learning Non-IID Big Data Longbing Cao
Tutorial 3
Nov 3, 2014
1:30-5:30pm
Data Analytics in Healthcare: Problems, Challenges and Future Directions Fei Wang
Xiang Wang
Tutorial 4
Nov 7. 2014
8:30-12:00am
E-commerce Personalization at Scale Elizabeth F. Churchill
Atish Das Sarma
Ranjan Sinha
Tutorial 5
Nov 7, 2014
8:30-12:00am
Learning to Hash with its Application to Big Data Retrieval and Mining Wu-Jun Li
Tutorial 6
Nov 7, 2014
1:30-5:00pm
Deep Learning for Natural Language Processing: Theory and Practice Xiaodong He
Jianfeng Gao
Li Deng
 
Tutorial 1: Crowdsourcing in Information and Knowledge Management
Lei Chen (HKUST, China)
Dongwon Lee (Penn State University, USA)
Meihui Zhang (National University of Singapore, Singapore)
URL: https://sites.google.com/site/cikmtutorial/
 
Longbing Cao (University of Technology Sydney, Australia)
URL: http://www-staff.it.uts.edu.au/~lbcao/publication/noniidness-learning-online.pdf
 
Fei Wang (IBM T. J. Watson Research Center, USA)
Xiang Wang (IBM T. J. Watson Research Center, USA)
 
Elizabeth F. Churchill (eBay Research Labs, USA)
Atish Das Sarma (eBay Research Labs, USA)
Ranjan Sinha (eBay Research Labs, USA)
URL: http://labs.ebay.com/cikm2014-tutorial/index.shtml
 
 

Tutorial 1: Crowdsourcing in Information and Knowledge Management

Abstract: As a novel computation paradigm, crowdsourcing is being actively pursued in diverse academic disciplines. Within computer science, many sub-fields have embraced the concept of crowdsourcing with open arms and applied the concept to solve many challenging problems. Communities relevant to the CIKM conference such as Database, Information Retrieval, and Data Mining are no exception to this phenomenon and there have been many exciting new results using crowdsourcing appearing in recent literature. This tutorial, after gentle introduction on the history and concept of crowdsourcing, provides the overall landscape of crowdsourcing research in CIKM communities, with the particular focus on some of latest crowdsourcing research in Database field.

Presenter 1: Lei Chen, Hong Kong University of Science and Technology, China (leichen@cse.ust.hk)

Lei Chen received the BS degree in computer science and engineering from Tianjin University, Tianjin, China, in 1994, the MA degree from Asian Institute of Technology, Bangkok, Thailand, in 1997, and the PhD degree in computer science from the University of Waterloo, Canada, in 2005. He is currently an associate professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. His research interests include crowdsourcing over social media, social media analysis, probabilistic and uncertain databases, and privacy-preserved data publishing. So far, he published over 200 conference and journal papers. He got the best paper awards in DASFAA 2009 and 2010. He is PC Track chairs for SIGMOD 2014, VLDB 2014, ICDE 2012, CIKM 2012, SIGMM 2011. He has served as PC members for SIGMOD, VLDB, ICDE, SIGMM, and WWW. Currently, he serves as an associate editor for IEEE Transaction on Data and Knowledge Engineering and Distribute and Parallel Databases. He is a member of the ACM and the chairman of ACM SIGMOD China Chapter.

Presenter 2: Dongwon Lee, Penn State University, USA (dongwon@psu.edu)

Dongwon Lee is currently an associate professor in the College of Information Sciences and Technology (a.k.a. iSchool) of the Pennsylvania State University, USA. He obtained his Ph.D. in Computer Science from UCLA in 2002. From 1995 to 1997, he has also worked as a programmer at AT&T Bell Labs.  Working mostly on the issues arising in the management and mining of diverse forms of data (e.g., relational records, documents, XML, and social media), he has (co-)authored over 130+ scholarly articles in selective publication outlets in Databases and Data Mining. He has also served as a PC member for major venues such as SIGMOD, VLDB, ICDE, CIKM, KDD, SDM, WWW, IJCAI, WSDM, and JCDL. Further details of his research can be found at: http://pike.psu.edu/

Presenter 3: Meihui Zhang, National University of Singapore, Singapore (mhzhang@comp.nus.edu.sg)

Meihui Zhang is currently a Research Fellow at National University of Singapore and will join Singapore University of Technology and Design as an Assistant Professor in August. She did her Ph.D. in Computer Science at National University of Singapore and obtained her B.E. in Computer Science from Harbin Institute of Technology, China. Her research interests mainly focus on database issues. Her Ph.D. thesis was on database exploration and schema extraction. She also works on crowdsourcing, massive data integration and spatio-temporal databases.

 

Tutorial 2: Learning Non-IID Big Data

Abstract: Learning from big data is increasingly becoming a major challenge and opportunity for big business and innovative learning theories and tools. Two of the most critical challenges of learning from big data are the uncovering of the explicit and implicit coupling relationships embedded in mixed heterogeneous data from multiple sources. The coupling and heterogeneity of the non-IIDness aspects form the essence of big data and most real-world applications, namely the data is not independent and identically distributed (IIDness). However, most classic theoretical systems and tools in statistics, data mining, database, knowledge management and machine learning assume the independence and identical distribution of underlying objects, attributes and values. For this, non-IIDness learning in big data emerges as a critical theoretical problem and is recently focused, which considers the couplings and heterogeneity between entities, properties, interactions and contexts. In this tutorial, we present a comprehensive overview of the non-IIDness learning, and introduce general frameworks and algorithms for non-IID classification, clustering, ensemble clustering, text mining, and recommender systems, and their challenges and prospects.

Presenter: Longbing Cao, University of Technology Sydney, Australia (longbing.cao@uts.edu.au)