2021 International Conference on Digital City Construction and Information Technology (DCCIT 2021)
Keynote Speakers
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Prof. Hongzhi Wang

Harbin Institute of Technology, China

Research Area: Database Management System, Big Data Quality, Big Data Analysis and Mining, AI for DB, DB for AI, Industrial Big Data, Blockchain and Financial Big Data 

Introduction: Hongzhi Wang, Professor, PHD supervisor, the leader of massive data computing center and the vice dean of the honors school of Harbin Institute of Technology, the secretary general of ACM SIGMOD China, outstanding CCF member, a standing committee member CCF databases and a member of CCF big data committee. Research Fields include big data management and analysis, database systems, knowledge engineering and data quality. He was “starring track” visiting professor at MSRA and postdoctoral fellow at University of California, Irvine. Prof. Wang has been PI for more than 10 national or international projects including NSFC key project, NSFC projects and National Technical support project, and co-PI for more than 10 national projects include 973 project, 863 project and NSFC key projects. He also serves as a member of ACM Data Science Task Force. He has won First natural science prize of Heilongjiang Province, MOE technological First award, Microsoft Fellowship, IBM PHD Fellowship and Chinese excellent database engineer. His publications include over 200 papers in the journals and conferences such as VLDB Journal, IEEE TKDE, VLDB, SIGMOD, ICDE and SIGIR, 6 books and 3 book chapters. His PHD thesis was elected to be outstanding PHD dissertation of CCF and Harbin Institute of Technology. He severs as the reviewer of more than 20 international journal including VLDB Journal, IEEE TKDE, and PC members of over 50 international conferences including SIGMOD 2022, VLDB 2021, KDD 2021, ICML 2021, NeurpIS 2020, ICDE 2020, etc. His papers were cited more than 2000 times. His personal website is http://homepage.hit.edu.cn/wang.

Speech Title:  Big Data Cleaning: challenges and explorations

Abstract: In the age of big data, data quality problems become more serious, and data cleaning is in great demand. However, data cleaning in big data age brings news technical challenges including the mixed errors, absence of knowledge and computational difficulty. Facing the challenge of mixed errors, we discover the relationships among various types of errors and develop data cleaning algorithms for multiple errors. We also design data cleaning strategies with crowdsourcing, knowledge base as well as web search for the supplement of knowledge. For efficient and scalable data cleaning, we develop parallel data cleaning systems and efficient data cleaning algorithms. This talk will discuss the challenges of big data cleaning and overview our solutions. 


Prof. Jianqin Zhang

Deputy Director, Key Laboratory of Urban Spatial Information, Ministry of Natural Resources

Beijing University of Civil Engineering and Architecture

Research Area: Geographic information systems and smart city research, whose research direction are the Traffic Geographic Information Systems and Intelligent Transportation, etc

Introduction: He is a professor of Bejing University of Civil Engineering and Architecture, a member of the Working Committee of Theory and Method of Geographic Information Industry Association in China,a member of Bejing Intelligent Transportation Association. At present he mainly engaged in geographic information systems and smart city research, whose research direction are the Traffic Geographic Information Systems and Intelligent Transportation, etc.He carried out the research on traffic spatio-temporal big data modeling and deep learning mining analysis method, and developed the traffic spatio-temporal big data visualization mining analysis software system, large passenger flow monitoring and early warning system, traffic industry emergency command big data system and other application systems. The visualization of soil pollution big data holographic view, pollution evolution analysis and prediction machine learning methods were studied, and the site pollution big data information system was developed. Publish more than 60 papers, write 3 Aboriginal, 5 aboriginal software rights, 10 authorized invention patents, 5 teaching and science and technology awards, including 2 provincial and ministerial science and technology progress first prize, 2 second prize. 

His personal website is https://faculty.bucea.edu.cn/pub/zhangjianqin/ 

Speech Title:  Research and Development of Urban Traffic Brain (from the Perspective of Spatio-temporal Visual Computing)

Abstract: Traffic big data includes the total and panoramic data of people, vehicles, roads and environment. It is a typical spatiotemporal geographic information big data. This report discusses how to use traffic big data to mine the spatiotemporal distribution law of urban residents' travel, and how to develop digital twin technology to support the construction of urban traffic brain, In particular, it applies spatio-temporal visual mining and analysis technology and deep learning method to build an efficient and intelligent neural computing unit, including: distributed storage and parallel processing technology of traffic spatio-temporal big data; Multi source heterogeneous spatio-temporal big data integration and fusion technology; Traffic spatiotemporal big data mining and knowledge discovery technology; Traffic spatio-temporal big data visualization decision technology. As a result, it can turn big data into decision-making reference information of government management departments, operation reference information of transportation enterprises and service information of people's travel, so as to serve the realization of intelligent transportation.


Prof. Jiangfeng Wang

Beijing Jiaotong University

Research Area: Intelligent transportation system, Connected vehicle simulation and modeling, Digitalization  of transportation infrastructure

Introduction:  He presided over 4 national projects, including 3 NSFC and 1 MOST, and undertook more than 20 projects such as the Ministry of transport, the national development and Reform Commission and the Beijing Natural Science Fund, with a scientific research fund of more than 5.2 million yuan. Won 3 provincial and ministerial awards including the science and technology progress award of the Ministry of education, published more than 60 SCI / EI papers as the first / corresponding author, and authorized 6 invention patents as the first inventor. Edited 5 textbooks and participated in the preparation of national standards and transportation industry standards.

His personal website is http://faculty.bjtu.edu.cn/trans/8321.html

Speech Title: Traffic adaptability of CV driving behavior in V2V communication environment


With  the  development  of  the  Internet  of  vehicles  technology,  intelligence  and networking  will  become  the  main  characteristics  of  the  future traffic.  Under  the environment of vehicle-to-vehicle(V2V) communication, the information sharing ability of the connected vehicles(CV) makes their car-following behavior different from human vehicles (HV), and whether the car-following behavior can adapt to the complex road operation  environment  and  improve  the  efficiency  of  the  traffic  system  is  an  urgent problem to be studied.

Based on the analysis of the concept of traffic adaptability, the traffic adaptability of connected vehicles is studied from the perspective of person-vehicle-road, and puts forward the research prospect of traffic adaptability.