2019 3rd International Conference on Business Information Systems｜workshop of ICBDT 2019
Prof. Wen Jirong, Renmin University of China, China
Ji-Rong Wen is a professor and the dean of School of Information, Renmin University of China (RUC). He is also the Director of Beijing Key Laboratory of Big Data Research. His main research interests include big data management & analytics, information retrieval, data mining and machine learning. He received his Ph.D. degree in 1999 from the Institute of Computing Technology, the Chinese Academy of Science. Since then, he joined Microsoft Research Asia (MSRA) and once was a senior researcher and group manager of the Web Search and Mining Group at MSRA. He has contributed to many Microsoft products (e.g. Bing) and filed more than 50 U.S. patents. He was elected as a National “1000 Talents Project” Expert and joined Renmin University of China to lead the big data and AI research in 2013. He publishes extensively on prestigious international conferences and journals, and his papers have received more than 13,000 citations. He is and was the Associate Editor of ACM TOIS and IEEE TKDE, Honorary Chair of AIRS 2016, Conference Chair of CCIR 2017, Area Chair of SIGIR 2018, and PC Chair of SIGIR 2020.
Speech Title: Big Data, Semantics, Their Relationships, and Their Methodologies
Abstract: Big data analysis has been booming for a several years and various methodologies have been proposed to the solutions to big data analysis. Its purpose is to discover patterns of bid data, reveal new relationships/links among the big data, and thus predict what will happen in the future. At moment such big data analysis is beset by the “pure” data analysis methods without human being’s strong involvement and interference. An example in this direction is machine translation – learning to translate from massive translation instances. However, profound understanding of data, their relationships, and sematic discovery depend on the acquisition, understanding, and representation of concepts (the connotation of data), whereas the values of the data can be considered to be the extension. Various relationships among concepts, such as part-whole, internal-external, inter-layers, and “material-element” are a few typical semantic relationships that describe better the characteristics of big data and hence provide an innovative view toward big data analysis. This talk intends to open up a door to semantic analysis of big data as well as connections from data to concepts
Youliang Tian received the Ph.D. degree in cryptography from Xidian University. He was a postdoctor at the Institute of Information Engineering, Chinese Academy of Sciences and the winner of the Youth Science and Technology Award in Guizhou province. He is currently the Distinguished Professor and doctoral supervisor of Guizhou University, the academic leader of State Key Laboratory of Public Big Data, the deputy director of
Institute of Cryptography & Data Security and dean of the department of Cyberspace Security, Guizhou University, etc.
He is the Member of the Blockchain Specialized Committee of China Computer Society and the member of the Security Protocol Specialized Committee of Chinese Association for Cryptologic Research(CACR). He is the Vice Chairman of Youth Southwest BBS of Chinese Association for Artificial Intelligence(CAAI), Director of GuiZhou Computer Federation, Member of the Information Security Expert Committee of Guiyang State Secrecy Bureau, and Editorial Board Member of Journal on Communications and Chinese Journal of Network and Information Security.
His research interests include algorithm game theory, cryptography and security protocol, big data security and privacy protection, blockchain and electronic currency. In recent years, he has successively undertaken more than 10 projects including key projects of The National Natural Science Foundation of China, projects of The National Natural Science Foundation of China, joint research projects between the Ministry of Education and China Mobile, the Top Talents Support Project in Guizhou Province, the Big Data Technology List
Project in Guizhou Province, etc. He has published more than 50 academic papers in IEEE, SCIENCE CHINA and other domestic and foreign journals or international conferences, and applied for 16 patents for invention. He won the second and the third prizes of Science and Technology Progress Award and Youth Science and Technology Award, Guizhou Province.
Speech Title: Key Technologies of Big Data Security Protection
Abstract: Big data security and privacy protection technology is a key technical bottleneck restricting the opening, sharing and application of big data, and big data security protection system is a systematic security project. The report introduces the key technologies of big data security and privacy protection and focuses on the automatic de-identification technology of personal information, traceability technology and application cases..
Peng Cui is an Associate Professor with tenure in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. His research interests include network representation learning, causally-regularized machine learning, and social-sensed multimedia computing. He has published more than 100 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the IEEE Multimedia Best Department Paper Award, SIGKDD 2016 Best Paper Finalist, ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is the Associate Editors of IEEE TKDE, IEEE TBD, ACM TIST, and ACM TOMM etc.
Speech Title: Towards Explainable and Stable Prediction
Abstract: Predicting unknown outcome values based on their observed features using a model estimated on a training data set is a common statistical problem. Many machine learning and data mining methods have been proposed and shown to be successful when the test data and training data come from the same distribution. However, the best-performing models for a given distribution of training data typically exploit subtle statistical relationships among features, making them potentially more prone to prediction error when applied to test data sets where, for example, the joint distribution of features differs from that in the training data. Therefore, it can be useful to develop predictive algorithms that are robust to shifts in the environment, particularly in application areas where models cannot be retrained as quickly as the environment changes. In this talk, I will describe a view of this problem from correlation versus causality perspective, and describe a number of our recent efforts in this direction.
Dr. Wenyu Zhang is a full professor and dean at the School of Information, Zhejiang University of Finance & Economics, China. He received Bachelor degree in Zhejiang University, China in 1989 and Ph.D. in Nanyang Technological University, Singapore in 2002. He worked as a research fellow (2003 to 2004) in Singapore – Massachusetts Institute of Technology Alliance. His current research interests include business analytics, big data management, data mining, etc. He has published over 100 papers in international journals and conference proceedings. He has been principle investigator of three national projects supported by National Natural Scientific Foundation of China.
Speech title: Key technologies of stock forecasting models based on big data and crowd intelligence
Abstract: Along with the economic development, an increasing number of factors and high-order data have considerably influenced the fluctuations in stock market. Many existing studies have considered the spatio-temporal correlation of stock index but ignored the comprehensive influence of crowd intelligence on stock index with changes in time and space. Therefore, to improve the forecasting accuracy of the multi-factor and high-order time series stock forecasting models, some key technologies based on big data and crowd intelligence are explored, by combining knowledge graph, crowd sensing, semantic search, personalized recommendation, game theory, fuzzy time series, credit scoring, deep learning, etc. The experimental results demonstrate that the proposed model shows outstanding forecasting accuracy compared with the benchmark methods on the Shanghai Stock Exchange Composite Index and Taiwan Stock Exchange Capitalization Weighted Stock Index.