报告人:  熊辉（美国新泽西州立大学教授）

报告时间: 2016年5月25日（本周三）下午2：00

报告地点: 北京化工大学 化纤楼207

主办单位: 北京化工大学 经济管理学院

附：熊辉教授简历

Abstract.

Recent years have witnessed the big data movement throughout all the business sectors. As a result, awareness of the importance of data mining for business is becoming wide spread.  However, the big data are usually immense, fine-grained, diversified, dynamic, and sufficiently information-rich in nature, and thus demand a radical change in the philosophy of data ****ytics. In this talk, we introduce a set of scenarios for understanding and mining of business data in various business sectors. In particular, we will discuss the technical and domain challenges of big data ****ytics in business environments. The theme to be covered will include (1) the data mining problem formulation in different business applications, such as marketing, real estate, and mobile intelligence; (2) the challenging issues of data pre-processing and post-processing in business ****ytics; (3) how the underlying computational models can be adapted for managing the uncertainties in relation to big data process in a huge nebulous business environment. Finally, we will also show some promising research directions.

Professor Hui Xiong

Email: hxiong@rutgers.edu

WEB: http://datamining.rutgers.edu

Management Science and Information Systems Department

Rutgers Business School

Rutgers, the State University of New Jersey

Dr. Hui Xiong is currently a Full Professor and Vice Chair in the Management Science and Information Systems at Rutgers Business School and the Director of Rutgers Center for Information Assurance at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), and the ICDM-2011 Best Research Paper Award (2011). Dr. Xiong is a prominent researcher in the areas of business intelligence, data mining, big data, and geographic information systems (GIS). For his outstanding contributions to these areas, he was elected an ACM Distinguished Scientist in 2014. He has a distinguished academic record that includes 200+ referred papers and an authoritative Encyclopedia of GIS (Springer, 2008). He is serving on the editorial boards of IEEE Transactions on knowledge and Data Engineering (TKDE), ACM Transactions on Knowledge Discovery from Data (TKDD), ACM Transactions on Management Information Systems (TMIS), and IEEE Transactions on Big Data (TBD). Also, he  served as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM-2013), and a General Co-Chair for the IEEE 2015 International Conference on Data Mining (ICDM-2015).