报告题目：Turning Big Data to Big Knowledge: A Data-to-Network-to-Knowledge Paradigm
报告日期及时间：10月16日上午9点
报告地点： 计算机学院8楼报告厅
报告人： Jiawei Han 教授
报告人单位：University of Illinois at Urbana-Champaign

报告人简介：Jiawei Han, Abel Bliss Professor of Computer Science, University of Illinois at

Urbana-Champaign.  He has been researching into data mining, information network analysis,

database systems, and data warehousing, with over 700 journal and conference publications.

He has chaired or served on many program committees of international conferences, including

PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coordinator for VLDB

conferences. He also served as the founding Editor-In-Chief of ACM Transactions

on Knowledge Discovery from Data and is serving as the Director of Information Network

 Academic Research Center supported by U.S. Army Research Lab, and Director of

KnowEnG, a BD2K (Big Data to Knowledge) center supported by NIH.  He is a Fellow

of ACM and Fellow of IEEE. He received 2004 ACM SIGKDD Innovations Award,

2005 IEEE Computer Society Technical Achievement Award, and 2009 IEEE

 Computer Society Wallace McDowell Award.  His book "Data Mining: Concepts and

Techniques" has been used popularly as a textbook worldwide.

报告摘要： With huge amount of data mounting everywhere, it is essential to turn big data

 to big knowledge.  Massive amounts of data are natural language text-based,

unstructured, noisy, and untrustworthy, but are interconnected, potentially forming gigantic,

 interconnected information networks.  If such text-rich data can be processed and organized

 into multiple typed, semi-structured heterogeneous information networks, organized

knowledge can be mined from such networks.   Most real-world applications that

handle big data, including interconnected social networks, medical information systems,

online e-commerce systems, or Web-based forum and data systems, can be structured into

 typed, heterogeneous social and information networks.  For example, in a medical care network,

objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits,

diagnosis, and treatments are intertwined together, providing rich information and forming

heterogeneous information networks.  Effective analysis of large-scale, text-rich heterogene

us information networks poses an interesting but critical challenge.
 
In this talk, we present an overview of our recent studies on construction and mining of text-rich

heterogeneous information networks.  We show that relatively structured heterogeneous

information networks can be constructed from unstructured, interconnected, text data, and

such relatively structured, heterogeneous networks brings tremendous benefits for data mining. 

 Departing from many existing network models that view data as homogeneous graphs or

networks, the text-based, semi-structured heterogeneous information network model leverages

the rich semantics of typed nodes and links in a network and can uncover surprisingly rich

knowledge from interconnected data.  This heterogeneous network modeling will lead to the

discovery of a set of new principles and methodologies for mining text-rich, interconnected

data.  We will also point out some promising research directions and provide arguments on

that construction and mining of text-rich heterogeneous information networks could be a key 

to transforming big data to big knowledge.

邀请人： 彭智勇 教授