2019 3rd International Conference on Business Information Systems|workshop of ICBDT 2019

Keynote Speaker


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: Towards User-centric Information Seeking in the Intelligent Era
One of the fundamental problems in information science has become even more critical in intelligent era: how to identify objects satisfying a user’s information need. The goal is to present to the user only information that is of personalized interests, in an interactive and interpretable way. Unfortunately, to this goal, current information seeking techniques are still far from being as satisfied as expected. User’s information need is often complex and vague and most information services perform the seeing process mainly replying on relevance matching. In this talk, I will first highlight the key characteristics and challenges of information seeking in the intelligent era, and then introduce recent progresses made in my team at Renmin University of China. Specifically, I will introduce our research work on personalized search, interactive search, and explainable recommendation. Finally, I will discuss some directions for future research.

Prof. William Wei Song, Dalarna University, Sweden 
Dr. William Wei Song is a full professor in Information Systems and Business Intelligence at Dalarna University, Sweden, from Dalarna University in 2011 and was an academic staff member at Durham University in England. He received docent title from Stockholm University in Computer and Information Science in 2003.
Prof. Song received PhD in Computer and Information Sciences from Stockholm University and the Royal Institute of Technology in Sweden in 1995. Dr. Song was a senior researcher at E-Business Technology Institute of Hong Kong University, Hong Kong, China from 2000. He sits at the editorial board of international journals including International Journal of Information System Modeling and Design (IJISMD), International Journal of Knowledge Engineering and Data Mining (IJKEDM), International Journal of Knowledge Engineering (IJKE), and Advances in Computational and Applied Sciences (ACAS). He has been general chair, track chair and program committee chair of many international conferences workshops, and symposiums, including World Wide Web, WISE-QUAT series, and ICKET 2015, ICCIA 2017, and ISD series. He was keynote speaker at ICKET, WAST, ICCIA, etc. He has been reviewer of many scientific and technology funders, including ITF (Hong Kong), Vinnova (Sweden), EPSRC (UK), and ESPRIT (EEC) and FP7 (EU), and NSFC and 863 (China). In the last few years, Prof. Song has been principal or co-investigator of the following projects:
2018, EU Interreg, ecoINSIDE 2
2014, EU Project on energy
2011, ReliaWind, EU Project, focusing on error/fault detection of wind turbines in a large wind energy system.
2008, EPSRC Project Customers’ Requirements Analysis for British Telecom.

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


Prof. Wenyu Zhang, Zhejiang University of Finance and Economics, China

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.

Prof. Qiguang Miao, Xidian University, China

Prof. Qiguang Miao is Vice Dean of the School of Computer Science and Technology in Xidian University. He received the Ph.D. degrees in computer application technology from Xidian University in December, 2005. His research interests include Computer Vision, machine learning and Big Data. As principal investigator, he is doing or has completed 1 National Key R&D Program of China, 4 projects of NSFC, 2 projects of Shaanxi provincial natural science fund; more than 10 projects of National Defense Pre-research Foundation, 863 and Weapons and Equipment fund. He has hosted 1 project supported by Fundamental Research Funds for the Central Universities by MOE. In 2012, he was supported by the Program for New Century Excellent Talents in University by Ministry of Education. Dr. Qiguang Miao is the associate editor of International Journal of Bio-Inspired Computing, Neurocomputing, Journal of Memetic Computing, Multimedia Tools and Application, Journal of Industrial Mathematics, and Gate to Computer Vision and Pattern Recognition. He is also a committee member of CCF, a committee member of CCF Computer Vision. He has been the chairman of CCF YOCSEF (2017-2018).
In recent years, Dr. Qiguang Miao has published over 100 papers in the significant domestic and international journal or conference including IEEE International Conference on Computer Vision, IEEE Trans. on Image Processing, IEEE Trans. on Neural Networks and Learning Systems, IEEE Trans. on Geoscience and Remote Sensing, Journal of Visual Communication and Image Representation, Neurocomputing, Knowledge Based System, IET Image Processing, and so on.


Speech Title: The Gesture Recognition Driven by Big Data

Abstract: Gesture recognition has attracted great attention owing to its applications in many fields such as Human Computer Interaction. In recent years, the development of deep learning has promoted approaches of gestures recognition. However, the performance of deep learning techniques depends on the quantity and quality of training data. In other words, the big data plays a significant role in the deep learning-based gesture recognition.
This talk will share our experience on Large-scale video-based Gesture Recognition with an effective 3D CNN based method.  To use the big data to drive the gesture recognition, we first employ different modalities of data, including RGB, depth, saliency, and optical flow, and enhance them according their different characters. Then we leverage the 3D CNN to extract spatiotemporal features blended together in the next stage to boost the performance. Finally the classes are predicted with a linear SVM classifier. Our proposed method ranked the 1st place in two rounds of the Chalearn LAP Large-scale Gesture Recognition Challenge in 2016 and 2017.


LiZhen Cui, Professor, Doctoral Supervisor
Dean& Deputy Party Secretary, School of Software, Shandong University

Co-Director, Joint SDU-NTU Center for Artificial Intelligence Research(C-FAIR), Shandong University

Director, Research Center of Software and Data Engineering, Shandong University
Associate Director, National Engineering Laboratory for E-Commerce Transaction Technologies
Team: The Research Center of Software and Data Engineering

Dr. Lizhen Cui is a full professor and doctoral supervisor of Shandong University. Being a visiting scholar of Georgia Institute of Technology and a visiting professor of Nanyang Technological University, he is appointed dean and deputy party secretary for School of Software, Shandong University, co-director of Joint SDU-NTU Center for Artificial Intelligence Research(C-FAIR),
director of the Research Center of Software and Data Engineering, as well as associate director of the National Engineering Laboratory for E-Commerce Transaction Technologies. Currently, he serves as Member of the Academic Committee of the CCF TCSC, the Committee of the CCF TFBD, the Committee of the CCF TCDB, and the Committee of the CCF TCCC. He is Secretary-General of the Association for Crowd Science and Engineering(ACE), and member of the National Advisory Committee on Software Engineering Professional Teaching Steering of MOE.
Research Interest: Big data intelligence theory, Data mining, Wisdom science, Medical health big data AI applications, etc.


Speech title: Deep Data Analysis in Healthcare

Abstract: Nowadays, with the rapid development of the medical industry, the application of big data technology in the medical industry has also brought tremendous opportunities. Big data in healthcare have the following characteristics: multi-source, heterogeneous, polysemy and low quality. This report introduces many challenges and difficulties we met during our work, such as multi-heterogeneous data fusion, inconsistent medical concepts and so on. In order to solve these difficulties, we have made great efforts, and made great progress in our research work. Our research works can divided into seven aspects: fusion of big data in whole life cycle healthcare, big data in healthcare modeling and store base on big graph, quality assessment for big data in healthcare, construction of health map, medical concept representation learning and disease risk prediction, quality monitoring for medical services, clinical pathway pattern mining, etc. Making good use of these achievements will be of great value to medical diagnosis, decision aids, patient monitoring and government management.


Prof. Youliang Tian, Guizhou University, China

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..